Overview of position sensorless control for switched reluctance motor drive systems

Abstract

Sensorless control technology for switched reluctance motors (SRMs) is a critical research direction aimed at enhancing system reliability and reducing costs. This paper provides a systematic review of sensorless control strategies across different speed ranges. In the low-speed region, the focus is on pulse voltage injection methods, which extract rotor position information through high-frequency signal excitation, and unsaturated region flux linkage modeling approaches that estimate position by leveraging the linear relationship between flux linkage and phase current. In the medium-to-high-speed region, the review covers rotor-characteristic-position-based detection methods, position observer techniques, and the application of intelligent algorithms including neural networks and fuzzy logic in position estimation. Research indicates that while each method demonstrates strong adaptability under specific operating conditions, achieving high-precision estimation across the entire speed range and enhancing parameter robustness remain key challenges for future studies. Development trends suggest that deep integration of hybrid strategies and intelligent algorithms will further advance the performance of sensorless control systems.

Keywords

switched reluctance motor (SRM)sensorless control pulse voltage injection position observer intelligent algorithms

Introduction

The Switched Reluctance Motor (SRM) is a type of special electric motor characterized by its simple and robust structure, low manufacturing cost, ease of maintenance, and excellent fault tolerance capabilities.^1–9 Its unique doubly salient structure (where both stator and rotor feature salient poles), the design incorporating windings only on the stator without windings or permanent magnets on the rotor, and its inherent capability for wide speed regulation range, demonstrate significant application potential in fields such as electric vehicle propulsion, industrial automation equipment, aerospace auxiliary systems, household appliances, and power tools.^10–25 The operating principle of the SRM is based on the principle of minimum reluctance. The controller applies excitation currents to the stator phase windings in a specific logical sequence, generating reluctance torque by utilizing the characteristic that the air gap permeance varies with the rotor position.^26–36 Consequently, high-performance control of the SRM fundamentally relies on the precise angular coordination between the stator phase currents and the rotor position, necessitating the real-time and accurate acquisition of rotor position information.^37–46

Conventional SRM control architectures typically employ mechanical position sensors—such as optical encoders, resolvers, or Hall-effect sensors—mounted directly on the motor shaft to acquire real-time rotor position signals. Core control strategies, including Current Chopping Control (CCC) for low-speed, high-torque scenarios, Angle Position Control (APC) for high-speed efficiency optimization, and fundamental voltage Pulse Width Modulation (PWM) control, are critically dependent on the precision of these feedback signals to ensure accurate commutation timing and phase current regulation.^47–65

However, the incorporation of traditional mechanical position sensors also introduces a series of significant drawbacks. These disadvantages manifest primarily in increasing the overall cost and complexity of the drive system. The position sensors themselves and their associated interface circuits directly increase material and manufacturing costs. Furthermore, the additional sensor installation and cable connections increase system complexity and introduce potential points of electrical or mechanical failure. More critically, physical position sensors are often a weak link in system reliability. In harsh industrial environments—characterized by high temperature, high humidity, pervasive dust, oil contamination, strong vibrations, and intense electromagnetic interference—their performance and lifespan may deteriorate drastically. Once these sensors fail, it can lead to the complete collapse and shutdown of the entire motor control system, posing a significant threat to system safety.

Given the problems associated with mechanical position sensors, especially in application contexts emphasizing cost-effectiveness, high reliability, resistance to harsh environments, and miniaturization/integration, the development of SRM control technology that does not rely on physical position sensors has become highly necessary and urgent.^66–75 Sensorless control technology has emerged precisely to address this challenge. Its core concept is to utilize inherent and easily measurable electrical variables during SRM operation, real-time and accurately estimating the instantaneous rotor position and speed information through the design and application of advanced signal processing algorithms, state observers, or intelligent computational models, thereby completely replacing the function of physical sensors.^75–86

The development of high-performance sensorless control techniques for SRMs holds significant theoretical value and broad practical significance. It can effectively reduce the overall system cost, enhancing product market competitiveness by eliminating the sensor and its interface circuitry. More crucially, it substantially improves the overall reliability and robustness of the system, eliminating the potential for failure due to physical damage to sensors. This enables the drive system to withstand harsher and more variable operating conditions.^87–110

Given their critical role in cost-effective and robust drive systems, SRM sensorless control strategies have emerged as a persistent research hotspot in power electronics and electrical drives, catalyzing a wide array of technical solutions rooted in diverse physical principles and algorithmic innovations. This review systematically surveys and categorizes recent advancements in this field, providing a critical analysis of the underlying methodologies. The structure is organized as follows: First, mainstream sensorless techniques will be classified based on operational speed ranges, including low-speed and high-speed phases. Subsequently, a detailed analysis of sensorless control methods for different speed ranges will be provided, covering their fundamental principles and specific implementation schemes. Finally, the review will summarize persistent technical bottlenecks and challenges in current research, along with a forward-looking perspective on future development trends and research directions. This work intends to offer valuable references and insights for researchers, engineers, and readers interested in technological advancements in SRM sensorless control.

The composition of SRM drive system

Composition of SRM system

Figure 1 illustrates the key components of a typical sensorless drive system for a SRM. At its core, the SRM itself functions as the primary element for energy conversion. Its operation is powered by a DC source via a power converter. Driven by PWM signals generated by the controller, this power converter precisely switches current into each phase winding of the SRM to produce driving torque.^111–123 The intelligent core of the system resides in the controller. This component not only executes the core sensorless control algorithm but also performs real-time analysis of the electrical signals—such as phase currents or phase voltages—obtained from the SRM. By identifying inherent electromagnetic characteristics within these signals (e.g. inductance variations), the controller continuously and indirectly estimates the rotor’s precise position and speed information, completely eliminating reliance on external physical position sensors. Based on these estimations, the controller accurately generates the required PWM command signals. This enables closed-loop regulation of the power converter, ultimately ensuring smooth, efficient, and stable operating performance for the SRM across diverse operating conditions.^124–130

Figure 1.

A sensorless drive system for SRMs.

Figure 2 details the three core operating modes governing phase current dynamics, which are instrumental for sensorless control: the Exciting Mode establishes current rise, the Freewheeling Mode maintains continuity, and the Demagnetizing Mode facilitates rapid current collapse. While these modes are fundamental to SRM drives, their primary relevance to position estimation lies in their ability to shape the current transient—serving as the excitation carrier for high-frequency injection at low speeds and defining the observation window for back-Electromotive Force (EMF)/ﬂux linkage detection at high speeds. This topology not only achieves energy recuperation but also utilizes its steep current decay slope to accelerate phase current collapse for precise turn-off timing control, which is crucial for enhancing torque response speed and mitigating torque ripple. Through coordinated switching of the power devices, these three modes significantly influence the phase current’s transient response characteristics, providing the essential electromagnetic parameters from which sensorless algorithms extract critical rotor position features.^131–145

Figure 2.

Topology of asymmetric half-bridge converter: (a) conduction mode, (b) freewheeling mode, and (c) demagnetization mode.

Mathematical model of SRM

The motion control of the SRM can be achieved by energizing the phase windings according to specific excitation rules. Taking a three-phase SRM with a 12/8 pole structure as an example, the evolution of its electromagnetic states during rotation through an angular range equivalent to one pole pitch is clearly illustrated in Figure 3. Figure 3(a) depicts the initial state: the rotor salient pole is perfectly aligned with the center of the stator slot (also referred to as the stator tooth center). After the rotor rotates 22.5° counterclockwise, it reaches the second state shown in Figure 3, where the rotor salient pole becomes accurately aligned with the center of the stator pole. It is particularly noteworthy that within the rotor angular motion range from 0° to 22.5°, the inductance value of the corresponding phase winding is not constant. As the rotor position changes, continuously reducing the magnetic circuit reluctance (increasing permeance), the phase inductance gradually rises from its minimum value, reaching its peak when the rotor reaches the position aligned with the center of the stator salient pole (approximately 22.5°). Subsequently, the inductance exhibits a declining trend as the rotor continues rotating, until it enters the third state denoted in Figure 3 after rotating approximately another 22.5°. Furthermore, through precise finite element simulation methods, the static characteristic curves of flux linkage and torque versus rotor angular position under different phase current conditions can be obtained for the SRM. These key relationships are also intuitively reflected in Figure 3(b) and (c).^146–155

Figure 3.

SRM structure topology of a three-phase 12/8 poles and electromagnetic properties²⁵⁵: (a) rotor electrical angle, (b) flux linkage characteristic, and (c) torque characteristic.

Figure 3 systematically elaborates the core physical constraints and flux linkage modeling challenges inherent in SRM operation. As an energy conversion device governed by electromagnetic induction laws, the electrical dynamics of each winding in its multi-phase structure are strictly controlled by the electromagnetic equations, represented as (1). The key issue lies in the SRM’s doubly salient structure and variable air-gap characteristics, which cause the magnetic circuit to exhibit high non-linearity. This makes the flux linkage ψ(i, θ) a strongly coupled function of both phase current i and rotor position angle θ—a non-linear distortion further exacerbated by magnetic saturation effects under specific positions and high-current operating conditions.

ψ_{k} = e_{k} \cdot t

(1)

where ψ_k, e_k, and t represent the flux linkage, induced electromotive force and time of the kth phase winding, respectively. And k = 1, 2, …, m.

Although mathematical expressions can be used to approximate this flux linkage model, traditional fitting methods face significant theoretical limitations in achieving accuracy across the entire operating range: Firstly, it is difficult to precisely characterize multi-dimensional non-linear boundaries; Secondly, real-time calculations are prone to state drift induced by noise interference.

In engineering practice, the three-dimensional look-up table (3D-LUT) method is widely adopted as the core strategy for high-precision flux linkage acquisition. This method constructs a discrete dataset of ψ(i, θ) in advance, covering the full range of currents, position angles, and temperature conditions, based on finite element simulation or experimental measurement data. Continuous flux linkage estimates are then output through real-time coordinate mapping and interpolation calculations. This strategy essentially involves trading off storage space for computational complexity, thereby avoiding the numerical instability associated with solving non-linear equations online. It provides a reliable flux linkage observation foundation for sensorless algorithms, ultimately supporting the coordinated optimization of predictive current control and position observers.^156–165

There exists an intrinsic coupling relationship among flux linkage, rotor position, and current, as mathematically expressed in (2):

ψ_{k} = L_{k} (θ_{ph}, i_{k}) \cdot i_{k}

(2)

where L_k and i_k represent the phase inductance and phase current, respectively. θ_ph is rotor position.

The voltage balance equation is expressed by (3):

U_{k} = R_{k} i_{k} + \frac{d ψ_{k}}{dt}

(3)

where U_k and R_k represent the phase voltage and phase resistance, respectively.

The mechanical equilibrium equation is expressed by (4):

T_{e} - T_{L} = J \frac{d ω}{dt} + D ω

(4)

where T_e and T_L represent the electromagnetic torque generated by the motor and load torque, respectively. J and D are the constant parameters of the moment of inertia and viscous friction coefficient. ω is the actual speed of the motor.

Figure 3 reveals the multidimensional coupling characteristics and technological evolution logic of the SRM control system. At its core lies the construction of a dynamic mapping framework between physical quantities, where the interactions among electromagnetic torque, flux linkage, inductance, and rotor position parameters not only form the theoretical foundation for sensorless estimation algorithms but also profoundly empower the global optimization of motion control architectures. In the speed control domain, algorithm design directly governs the motor’s dynamic performance, with CCC suppressing torque ripple through current threshold constraints, APC expanding high-efficiency operating ranges via turn-on/turn-off angle modulation, and voltage chopping control (VCC) along with direct torque control (DTC) enhancing response speed from power modulation and flux linkage trajectory tracking perspectives, respectively, while torque sharing function (TSF)-based instantaneous torque control achieves smooth output across all operating conditions through multiphase torque coordination.^166–180

Higher-dimensional stability demands drive the fusion of signal fault tolerance and sensorless innovation, creating a deeply coupled closed-loop relationship where the position observer provides real-time rotor spatial state feedback while control algorithm outputs serve as excitation carriers for position estimation methods, forming a critical robustness barrier against sensor failures or noise interference.^181–194

The collaborative solving of electromagnetic-mechanical equations further unlocks performance leaps, with the strongly nonlinear, position-sensitive nature of flux linkage and inductance spurring a paradigm shift from analytical equation-based fitting to 3D magnetic digital twins that leverage full-range flux linkage-current-position surface interpolation to overcome traditional modeling limitations in saturation regions. This methodological evolution systematically reshapes sensorless architectures by reducing physical sensor dependence while enhancing position-solving accuracy and dynamic disturbance rejection in complex operating conditions, highlighting how multiphysics coupling across electromagnetic, mechanical, and control domains enables SRM systems to achieve self-adaptive optimization under cost, reliability, and performance constraints, with the integration of data-driven modeling and model-based control representing the next frontier for high-performance sensorless SRM drives.^195–208

An overview of recent developments in sensorless control of SRM

This study systematically reviews existing position estimation strategies to clarify technological evolution trajectories, reveal core characteristics, and identify future directions. All selected methods are derived from experimentally validated literature, with their performance being quantitatively evaluated through metrics such as speed regulation range or estimation error, thereby establishing an empirical benchmark for optimizing sensorless control architectures. The comparative analysis demonstrates how different approaches address fundamental challenges in SRM control-including nonlinear magnetic saturation effects, parameter sensitivity, and real-time computational constraints-while highlighting emerging trends that integrate data-driven techniques with traditional model-based methods to achieve robust position estimation across diverse operating conditions. Throughout this review, the operational speed ranges are classified based on the availability of back-EMF information. Unless otherwise specified, low-speed operation refers to conditions below 15%–20% of the motor’s base speed. Medium-high-speed operation corresponds to speeds above this threshold, extending up to the maximum speed limit of the drive system.

Sensorless control strategies for low-speed operation

In the SRM starting and extremely low-speed operating range, conventional back-EMF methods become ineffective. The sensorless control strategies in this regime primarily rely on extracting the motor’s inherent magnetic characteristics as position information sources. By applying detection signals to non-excited phases or utilizing subtle features in phase currents, these methods identify electrical quantities sensitive to rotor position, thereby enabling accurate position estimation. This approach achieves zero-speed starting and stable low-speed operation without dependence on mechanical sensors.^209–215

Pulse injection method

The pulse injection method is a key strategy for achieving sensorless control of switched reluctance motors in low-speed and zero-speed ranges. Its core idea involves artificially injecting short-duration high-voltage pulse signals into non-conducting phases. Due to the highly nonlinear magnetization characteristics caused by the doubly salient structure of switched reluctance motors, the saturation level of the core material changes significantly with rotor position. When a high-voltage pulse is applied to a non-conducting phase winding, if the magnetic circuit of that phase is in an unsaturated state, the current will rise slowly; conversely, if the magnetic circuit is near saturation, the current will rise rapidly. Therefore, by precisely detecting and analyzing the current response induced by the injected pulse within a short time, the transient inductance information of that phase winding can be extracted, thereby deducing the real-time position of the rotor pole relative to the stator pole of that phase. Unlike back-EMF-based techniques, this approach exploits the inherent nonlinearity of the salient-pole magnetic circuit as its physical basis for position detection. By actively injecting probing signals to excite the motor’s transient inductance characteristics, it overcomes the limitations of traditional methods at standstill and extremely low speeds, ensuring reliable startup and operation in low-speed regimes. Typical implementations encompass voltage pulse train injection and current response feature extraction, often supplemented by position lookup tables. The practical viability of pulse injection strategies has been extensively validated on hardware platforms beyond laboratory simulations. Notably, these methods have been successfully deployed in electric vehicle powertrains and light electric vehicles (LEVs), where the high signal-to-noise ratio at standstill ensures reliable startup under heavy inertial loads. Furthermore, industrial implementations utilizing high-power converters have demonstrated robust performance in driving centrifugal compressors and pumps, confirming that the injected torque ripple does not significantly compromise the overall drivetrain efficiency in practical applications.

Cai et al.²¹⁶ proposed an innovative sensorless starting scheme that employs a dual-inductance dynamic threshold algorithm for the initial phase selection process. This algorithm accurately identifies the optimal starting phase by dynamically adjusting the decision threshold through real-time comparison of inductance characteristics among the three-phase windings (specifically manifested as inductance value differences between each pair of phases). Guo et al.²¹⁷ innovatively proposed a three-current-threshold detection method to address the specific limitations of low-cost resistive current sampling schemes. This method solves the core problem of traditional approaches being unable to detect phase current during demagnetization by setting three critical current thresholds: the first threshold precisely determines the turn-on moment, the second threshold reliably identifies the turn-off moment, while the newly added third threshold (set as a minimal current value close to zero) specifically detects whether the demagnetization current has completely decayed to zero. This triple-threshold mechanism not only preserves the structural simplicity of conventional dual-threshold methods but also enables the system to immediately trigger new current pulse injections after the current sampling blind zone ends through the introduction of demagnetization state monitoring. Guo²¹⁸ proposed an innovative high-precision injected current sampling scheme to address the critical issue of controller noise severely affecting current sampling accuracy in cost-sensitive applications. Based on the traditional direct-drive low-speed sensorless control framework, this study achieved significant improvements in current sampling accuracy through three key technological breakthroughs: First, a quantitative analysis model of traditional sampling errors was established, revealing the intrinsic relationship between noise interference and position estimation errors. Second, a novel dual-channel current sampling circuit was designed, using differential signal processing technology to reduce sampling noise to 23% of conventional single-channel solutions. Finally, the proposed pulse current peak prediction algorithm based on curve intersection method effectively compensated for time delay errors caused by discrete sampling.

Xiao et al.²¹⁹ addressed the inherent limitations of traditional fixed-amplitude pulse injection methods in low-speed sensorless control by proposing an intelligent pulse modulation scheme based on terminal sliding mode control (TMSC). Conventional approaches, which employ constant-amplitude pulse voltage injection, result in nonlinearly varying induced currents in unexcited phases with excessive amplitudes. This not only generates significant negative torque but also severely compromises control performance. To resolve this issue, the authors innovatively developed a pulse amplitude regulator based on TMSC. This regulator dynamically adjusts the pulse voltage amplitude through a nonlinear control law, maintaining the induced current at minimal levels throughout the entire unexcited phase period. The distinctive advantage of this solution lies in its complete elimination of adverse effects from motor parameter uncertainties—requiring neither pre-acquired flux linkage characteristics of the switched reluctance motor nor compromising practical engineering feasibility.

The methodological architecture proposed in this paper is fully presented in the overall block diagram shown in Figure 4. This integrated system optimizes low-speed operational performance by regulating pulse injection amplitude through a terminal sliding mode controller (TSMC). The left side of the diagram comprises the signal acquisition module, which detects the idle-phase current peak in real time and compares it with a reference value to generate an error signal. The central TSMC core receives this error signal along with approximate inductance values, dynamically adjusting the pulse voltage amplitude through a nonlinear control law to drive the PWM module for generating adaptive pulses. The right side employs a regional phase-locked loop (RPLL) estimator, utilizing the calculated inductance values to estimate rotor position and speed, which are fed back to the speed-current dual-loop controller to form closed-loop control. This architecture replaces traditional fixed-amplitude injection with online TSMC regulation, enhancing the signal-to-noise ratio at aligned positions while reducing copper loss and torque ripple at unaligned positions. It operates without reliance on motor magnetic characteristic parameters, acquiring key parameters solely through a self-tuning process, demonstrating strong robustness and universality. Notwithstanding these advancements, a fundamental trade-off persists in pulse injection strategies between estimation accuracy and injected torque ripple; while adaptive modulation schemes (e.g. TMSC) effectively suppress negative torque, they inevitably increase algorithmic complexity compared to fixed-amplitude injection methods, necessitating careful selection based on application-specific torque ripple tolerance.

Figure 4.

Overall block diagram of the TSMC based pulse injection scheme.

Unsaturated inductance model method

The unsaturated inductance model method represents one of the core modeling strategies for sensorless control of SRMs at low speeds. Its fundamental principle leverages the distinctive characteristic that winding inductance depends predominantly and uniquely on rotor mechanical position when the motor operates in the non-saturated region. Through in-depth analysis of the doubly salient magnetic circuit structure and reluctance variation patterns, this model analytically establishes a relatively simple explicit mathematical relationship between winding inductance and rotor angle as the foundation for position observers. During operation, the system continuously monitors or calculates the inductance value of the phase under observation, then substitutes this identified inductance value into the predefined inductance-position relational model to directly derive real-time rotor position information. This method offers distinct advantages including clear model structure, high computational efficiency, and ease of engineering implementation, making it particularly suitable for low-speed operation scenarios featuring light loads, low current levels, and gradual speed variations. Frequently employed as either a robust complement or simplified alternative to other algorithms in specific operational ranges, it also serves as a fundamental research tool for investigating intrinsic inductance-position relationships. The reliance on inductance linearity makes this method particularly suitable for medium-to-low power industrial drives where magnetic saturation is less pronounced. Published experimental results have shown that this approach maintains acceptable position estimation accuracy (within 2°–3°) even during load transients, making it a cost-effective solution for fan and blower applications where extreme precision is not the primary requirement.

Guo et al.²²⁰ proposed an innovative full-cycle unsaturated inductance offline automatic measurement method. This method achieves automatic rotor position adjustment without mechanical fixtures or position sensors through an original single-phase current chopping (SPCC) and dual-phase current chopping (DPCC) combined control strategy. During the measurement phase, the system simultaneously injects high-frequency pulses into all three-phase windings, precisely identifying each phase’s inductance characteristics using the current slope difference (CSD) method, and establishes a complete mapping relationship between unsaturated inductance and rotor position through Clarke transformation and arctangent operations. When transitioning to normal operation, this method only requires injecting a single high-frequency pulse into the idle phase, enabling rapid rotor position calculation through real-time inductance measurement and linear comparison with pre-stored characteristic curves, significantly improving computational efficiency.

However, due to the existence of magnetic saturation regions, the above method may increase rotor position estimation errors. To address the problems caused by magnetic saturation interference, many scholars have turned their attention to the idea of unsaturated inductance reconstruction.

Sun et al.²²¹ proposed an innovative model reshaping and self-calibration scheme. The study first conducted an in-depth analysis of the influence mechanism of magnetic saturation on the position estimation accuracy of traditional linear inductance models. Based on this analysis, two complementary technical approaches were developed: leveraging the constant-sum characteristic of phase inductances in unsaturated states for three-phase 12/8-pole SRMs, a virtual inductance construction strategy was designed to effectively circumvent nonlinear effects in magnetic saturation regions through algebraic transformations; to enhance the scheme’s applicability to multi-phase srms, an innovative real-time equivalent position triangle was established to identify position errors caused by magnetic saturation through geometric relationships, along with the development of a self-calibration technique based on inductance linearity.

Cai et al.²²² aims to solve the saturation effect problem caused by high currents during SRM startup, thereby achieving reliable sensorless starting. This method combines two innovative algorithms: unsaturated inductance reconstruction based inductance vector coordinate transformation (UIR-IVCT) and incremental inductance partitioning (IIP) to enable continuous rotor position estimation under saturated conditions while providing failure tolerance capability during single- or two-phase faults.

The core methodological flow is clearly presented in the overall block diagram Figure 5, which depicts the hybrid strategy’s architecture. First, the incremental inductance measurement equation (5) serves as the starting point, calculating incremental inductance through high-frequency pulse injection and current slope difference to circumvent saturation effects. While equation (5) provides a precise analytical expression for incremental inductance, its practical implementation in digital controllers relies heavily on high-resolution timers and fast ADCs to capture the current slopes (di/dt) within microseconds. The subtraction operation amplifies noise sensitivity, necessitating careful tuning of sampling intervals in hardware. The UIR-IVCT algorithm achieves initial positioning and continuous position estimation, supplemented by IIP algorithm for failure scenarios. Position estimation is represented by (6), which computes rotor position using orthogonal signals after inductance vector coordinate transformation.

L_{inc} = \frac{2 U_{bus} + 2 (V_{D} - V_{T})}{d i_{k} / {dt |}_{ON} - d i_{k} / {dt |}_{OFF}}

(5)

where L_inc is the incremental inductance, U_bus is the bus voltage, V_T and V_D represent the voltage drops across the power switch and diode, respectively.

θ_{est} = \frac{22.5}{π} [\frac{7 π}{6} + \arctan \frac{L_{β}}{L_{α}}]

(6)

where θ_est is the estimated rotor position, L_α and L_β are derived from the normalized inductance vector.

Figure 5.

Hybrid sensorless starting architecture combining UIR-IVCT and IIP algorithms.

This hybrid strategy demonstrates complementarity: UIR-IVCT relies on three-phase inductance data for high-precision estimation, while IIP requires only a single inductance threshold to enable failure tolerance, thereby enhancing overall startup process reliability. However, the efficacy of unsaturated inductance models remains highly susceptible to magnetic saturation and temperature drift; although reconstruction techniques (e.g. UIR-IVCT) mitigate these effects, their performance still relies heavily on the assumption of a known linear region, limiting their robustness under heavy-load or high-temperature operating conditions.

Beyond these approaches, some scholars have significantly improved rotor position estimation accuracy by incorporating regional RPLL technology.

Xiao et al.²²³ addressed the dependency of traditional low-speed sensorless control schemes on offline magnetic characteristic measurements by proposing an innovative adaptive position estimation method. The core of this method lies in the development of RPLL technology, which achieves precise position estimation through the coordinated operation of two key phases: during the initialization phase, the system employs a self-tuning process to automatically acquire unsaturated inductance characteristics, eliminating the need for offline measurements required by conventional methods; during operation, it utilizes idle-phase inductance information combined with the RPLL algorithm to achieve full-cycle rotor position tracking. The solution specifically designs a reversible speed detection mechanism based on heterodyne principles, effectively resolving the failure issues of traditional methods during speed reversal. Theoretical analysis demonstrates that this algorithm guarantees local stability and convergence.

Song et al.²²⁴ proposed a novel sensorless control strategy for SRMs operating under heavy-load and low-speed conditions, with its core innovation combining wide-range modeling of idle-phase inductance and a proportional-gain-decreasing second-order generalized integrator (SOGI-FLL-P). Conventional methods often fail in position estimation due to saturation effects, while piecewise position reconstruction causes abrupt changes at connection points, leading to speed fluctuations. To address these issues, the authors first inject high-frequency pulses into idle phases and calculate unsaturated inductance based on current slope differences, as expressed by (7):

L_{un} = L_{ph} = \frac{2 U_{dc} + 2 (U_{D} - U_{T})}{{\frac{d i_{ph}}{dt} |}_{on} - {\frac{d i_{ph}}{dt} |}_{off}}

(7)

where L_un is the unsaturated inductance, L_ph is the phase inductance.

This formula avoids saturation zone effects and provides the foundation for position estimation. Further, a third-order polynomial is utilized to establish the inductance-position mapping model, as expressed by (8):

θ_{est} (L_{i}) = a_{1} L_{i}^{3} + a_{2} L_{i}^{2} + a_{3} L_{i} + a_{4}

(8)

where i = A, B, or C. a₁, a₂, a₃, and a₄ are coefﬁcients of the function.

The core control architecture is fully presented in the overall block diagram (Figure 6). The left side of the diagram shows high-frequency pulse injection into idle phases to generate current responses, with the inductance model outputting initial position values; the central core is the SOGI-FLL-P module, which simultaneously achieves position filtering and speed estimation through dynamically attenuated proportional gains, eliminating phase lag and abrupt changes inherent in traditional differential methods; the right-side closed-loop control generates drive signals through a PI speed regulator and hysteresis current controller, supporting heavy-load transient scenarios.

Figure 6.

SOGI-FLL-P-based control architecture.

Sensorless control strategies for medium-high speed operation

During medium-high speed operation, the back-EMF signal generated by SRMs becomes sufficiently strong, serving as a crucial information source for high-precision sensorless control. The core strategy focuses on effectively extracting and utilizing flux linkage or back-EMF characteristics of phase windings, with mainstream technical approaches including reconstructing flux linkage trajectories through real-time integration of measured phase voltage and current, then estimating position based on their inherent relationship with rotor position, and designing high-performance state observers that leverage electromagnetic dynamic equations to achieve real-time rotor position/speed identification and tracking through closed-loop error feedback between model outputs and actual measurements. Concurrently, an efficient rotor-characteristic-position-based method finds widespread application. This approach capitalizes on the periodic variation of SRM phase inductance with rotor angle, which exhibits distinct maximum and minimum points. By continuously monitoring instantaneous inductance or derived physical quantities of each phase winding, these characteristic inductance variation points are detected and captured. These characteristic points serve as natural “anchors” for positioning, enabling continuous position estimation across the full electrical angle range when combined with speed information for interpolation or when working synergistically with the aforementioned state observers. Medium-high speed solutions demonstrate relatively strong parameter robustness, high precision, and fast dynamic response, significantly reducing the need for additional signal injection. They form the core foundation for reliable operation of modern SRM sensorless systems in medium-high speed ranges.^225–233

A. Rotor-characteristic-position-based method

In the medium-high speed operating range, rotor-characteristic-position-based sensorless strategies directly capture transient physical quantity mutation signals generated when the rotor passes specific geometric positions. These mutations typically manifest as zero-crossings or extremum points in the rate of change of phase winding inductance or derived physical quantities, exhibiting high sensitivity and determinacy. The system continuously detects and precisely timestamps these mutation events, correlating them with instances when the rotor crosses predefined critical angular positions. Subsequently, by incorporating the motor’s current average operating speed, continuous rotor position signals can be generated through linear or simple model-based position interpolation between adjacent characteristic points. This strategy eliminates the need for complex model identification and integration operations, demonstrates high tolerance to parameter variations, and delivers excellent execution efficiency, making it particularly suitable as a cost-effective implementation solution for position feedback in medium-high speed ranges.

Cai et al.²³⁴ proposed a sensorless method based on phase inductance slope characteristics. The core of this method lies in real-time detection of negative zero-crossings in the rate of change of motor phase winding inductance (specifically, the particular zero point where phase inductance slope transitions from positive to negative), enabling the system to accurately capture the critical position information when rotor and stator teeth become perfectly aligned. By utilizing these periodically captured alignment positions as reference points and combining them with timing logic during motor operation, the method further achieves estimation of instantaneous rotor angle and rotational speed. The advantage of this approach is its utilization of distinct characteristic points in inductance slope for position capture, providing a reliable foundation of rotor position and speed information for sensorless control.

Sun et al.²³⁵ proposed a medium-high speed sensorless control strategy based on unaligned rotor position estimation. This scheme achieves position estimation by detecting negative zero-crossings of phase reluctance slope, employing high-frequency pulse injection for CCC mode and conduction angle optimization for APC mode respectively. The study also designed a smooth mode-switching strategy and a fault-tolerant solution based on faulty-phase skipping, validating the effectiveness of the method on a six-phase SRM experimental platform.

Zhou et al.²³⁶ addressed the limitations of traditional methods in demagnetization zone sensitivity, aligned position estimation accuracy, and fault tolerance by integrating phase inductance gradient detection and idle-phase inductance threshold comparison techniques. The core innovation of this solution lies in adopting a demagnetization inductance slope negative zero-crossing (D-ISNZ) detection method to avoid misestimation in the main excitation region, combined with a single inductance threshold (SIT) remedial strategy to achieve robust control across wide speed ranges. Experiments conducted on a three-phase 12/8 SRM validate its effectiveness under full-speed operation (including heavy-load conditions) and phase-loss faults, requiring no prior magnetic characteristic data or complex computations, significantly enhancing system reliability. The phase inductance is calculated as follows:

L_{k} (i_{k}, θ) = {\begin{matrix} \frac{\int (U_{k} - i_{k} R_{k}) dt}{i_{k}}, & (i_{k} > 0) \\ 0, & (i_{k} = 0) \end{matrix}

(9)

The real-time inductance value when current is greater than zero is obtained by integrating the difference between phase voltage and resistive voltage drop, then dividing by current; when current equals zero, inductance is set to zero to avoid invalid calculations. This formula provides fundamental inductance data for position estimation, whose accuracy directly affects the subsequent slope detection and threshold comparison processes. The inductance slope is calculated as follows:

\frac{d L_{k}}{dt} = \frac{L_{k} (n + 1) - L_{k} (n)}{T_{s}}

(10)

where L_k (n + 1) and L_k (n) are the calculated phase inductances at the (n + 1)th and nth sampling instants, respectively, and T_s is the inductance sampling period.

By differencing inductance values at adjacent sampling instants and dividing by the sampling period Δt, the rate of inductance change is quantified. This formula serves as the core of the D-ISNZ method, enabling detection of negative zero-crossings in inductance slope during the demagnetization region to determine aligned positions, thereby avoiding false triggers caused by current fluctuations or saturation in traditional methods.

Figure 7 illustrates the overall control architecture of this method. As shown in Figure 7, the left section represents the high-frequency pulse injection module, which calculates inductance through idle-phase current responses; the central core algorithm module incorporates D-ISNZ detection and SIT remedial strategies, adaptively selecting estimation methods based on demagnetization current states; the right section constitutes the closed-loop control module, generating drive signals through a PI speed regulator and hysteresis current controller to support dynamic speed adjustment and fault tolerance. The block diagram highlights multi-module collaboration: D-ISNZ prioritizes aligned position detection, switches to SIT upon failure, and achieves healthy-phase data fusion under phase-loss conditions through fault flags, ensuring continuous position estimation.

Figure 7.

Synthetic inductance detection strategy architecture.

B. Position-observer-based method

Currently, position-observer-based strategies represent a focal point in sensorless control research. This approach treats the motor itself as a dynamic system and constructs its mathematical model as the core of the observer. Operating in parallel with the actual motor, the observer receives measured voltage and current as inputs, and outputs optimal estimates of internal states—including rotor position and speed—in real time. Its essence lies in a closed-loop correction mechanism that continuously compares observer outputs with actual measurements, utilizing this error to dynamically adjust the observer’s internal states and force convergence toward true values. This model-based feedback correction mechanism endows the algorithm with exceptional dynamic tracking performance and robust disturbance resistance, effectively suppressing interference from parameter variations and measurement noise. As such, it stands as a mainstream advanced solution for achieving high-performance, high-precision sensorless operation. Observer-based techniques have demonstrated significant potential in high-power traction applications, particularly in scenarios demanding wide speed ranges. Field tests on electric haulage shearers and mining machinery have confirmed that flux-linkage observers can operate reliably in high-temperature and high-dust environments where mechanical sensors typically fail. Additionally, the compatibility of these observers with digital signal processors (DSPs) has facilitated their integration into commercial variable frequency drives, bridging the gap between academic research and industrial deployment.

Shao et al.²³⁷ addressed issues such as low model utilization and discrete position estimation in characteristic-flux-based sensorless drive methods by proposing a position-adaptive dual-loop observer based on a nonlinear decoupling model. The solution first establishes a nonlinear decoupled flux linkage model to reconstruct continuously linearized position-dependent functions; subsequently, a position adaptation law is designed to enable the reconstructed function to track the actual rotor position in real time. Through coordinated regulation of the dual-loop structure, continuous position estimation is achieved while effectively suppressing high-frequency noise interference.

Sun et al.²³⁸ addressed the challenges of dynamic response and estimation accuracy in sensorless control of SRMs by proposing a composite control strategy based on an improved sliding mode controller. The solution achieves performance enhancement through two key innovations: first, a time-varying-function-based improved sliding mode speed controller (TVSMSC) was designed to effectively suppress internal parameter disturbances and external load interference, while significantly reducing initial peaks during startup through a novel reaching law; second, a sliding mode position observer (SMPO) based on an inductance model was developed to achieve real-time precise estimation of rotor position and speed. Experimental results demonstrate that the proposed strategy ensures strong system robustness while significantly reducing speed overshoot and transient response time, while maintaining high position estimation accuracy. This approach of combining improved control algorithms with advanced observers provides an effective solution for high-performance sensorless control of SRMs.

Xiao et al.²³⁹ achieved full-cycle position estimation independent of magnetic characteristics through a quadrature flux estimator (QFE). Traditional methods relying on pre-measured flux linkage lookup tables face challenges such as time-consuming offline measurements, substantial storage requirements, and parameter sensitivity. This study utilizes the band-pass characteristics of QFE to eliminate DC offsets and harmonic components in flux linkage, transforming highly nonlinear flux signals into clean quadrature sine-cosine position signals. These signals are then processed by a three-phase phase-locked loop (PLL) to extract rotor position and speed. This method requires no prior magnetic characteristic data.

The core of this method lies in flux linkage calculation and quadrature signal generation. The real-time flux linkage calculation formula is expressed as follows:

λ (θ, i) = {\begin{matrix} \int (u - Ri) dt, & (θ \in [θ_{ON}, θ_{OFF}]) \\ 0, & (θ \in else) \end{matrix}

(11)

where θ_ON and θ_OFF are the turn-on and turn-off angles, respectively.

This piecewise function defines the flux linkage calculation method within the excitation interval: during the turn-on angle to turn-off angle range, real-time flux linkage is obtained by integrating the difference between phase voltage and resistive voltage drop; in non-conduction regions, flux linkage is set to zero to avoid invalid calculations. The integration process effectively suppresses current ripple effects, providing raw signals for subsequent QFE processing. The innovation of this formula lies in relying solely on circuit parameters rather than magnetic characteristic parameters, establishing the foundation for magnetic-characteristic-independent estimation. The formula for quadrature flux generation is expressed as follows:

{\begin{matrix} λ_{f} = - λ_{mag} \cos (N_{r} θ) \\ λ_{fq} = - λ_{mag} \sin (N_{r} θ) \end{matrix}

(12)

where λ_f denotes the fundamental ﬂux, λ_fq denotes the quadrature of the fundamental ﬂux λ_f, λ_mag is the magnitude of fundamental ﬂux.

The transformation into quadrature flux signals is not merely a mathematical manipulation; it is an engineering strategy to convert a nonlinear tracking problem into a linear PLL problem. This significantly simplifies the software architecture and enhances noise immunity compared to direct arctangent calculations of raw flux linkage data.

This pair of quadrature signals completely strips the DC offsets and harmonics from the flux linkage, encoding rotor position information in the phase rather than the amplitude, thereby simplifying position estimation to a standard phase-locked loop (PLL) problem. The breakthrough of this formula lies in transforming the nonlinear estimation problem of SRMs into a linear system tracking problem, eliminating the dependency on flux-linkage-position-current three-dimensional models.

Figure 8 illustrates the overall control architecture of this method. The block diagram reveals the complete process from signal acquisition to position estimation: the left section shows the flux linkage calculation module, which obtains raw flux linkage through voltage and current sampling; the central core innovation is the QFE module, which receives flux linkage signals and outputs filtered quadrature flux linkage; the right section constitutes the position generation module, where a three-phase PLL converts quadrature flux linkage into position and speed signals. The diagram highlights three key characteristics: first, full-period capability, achieving continuous 360° estimation through three-phase signal splicing; second, adaptive ability, feeding estimated speed back to QFE for center frequency adjustment; third, dynamic compensation, where output positions undergo lead compensation to eliminate digital control delays.

Figure 8.

Control architecture of the magnetic-characteristic-free sensorless strategy based on QFE.

This system overcomes the dependency on magnetic characteristics inherent in traditional methods by leveraging the frequency-selective properties of QFE, combined with logical triggering design to address the discontinuous flux linkage issue in SRMs. Ultimately, it achieves accuracy comparable to magnetic-characteristic-based methods without requiring any offline measurements, providing a universal solution for SRM sensorless control.

C. Intelligent-algorithm-based methods

Intelligent-algorithm-based sensorless strategies aim to address the challenges posed by the highly nonlinear and model-imprecise nature of SRMs. These methods circumvent the reliance on precise analytical models by leveraging data-driven tools such as neural networks, fuzzy logic, or support vector machines. Through offline or online learning, they directly construct complex nonlinear mappings from easily measurable electrical quantities to rotor position. Well-trained intelligent models exhibit strong nonlinear fitting capabilities and fault tolerance, effectively suppressing disturbances and parameter variations. This approach offers a promising pathway for robust position estimation under complex operating conditions. However, their performance heavily depends on the quality and completeness of training data, and they often face engineering implementation challenges due to high computational resource demands.^240–250 Despite concerns regarding computational complexity, recent advancements in edge-AI hardware have enabled the real-time deployment of neural network estimators. Experimental validations on automotive-grade SRM prototypes have shown that optimized neural networks can achieve position estimation errors comparable to traditional model-based methods while offering superior resilience to parameter variations, paving the way for next-generation autonomous driving actuators.

Cai et al.²⁵¹ addressed the strongly nonlinear relationship between rotor position and electromagnetic parameters in SRMs by proposing a neural network modeling method based on measured flux linkage characteristics. Through comparative analysis of improved backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) modeling performance, it was found that the BPNN utilizing the Levenberg-Marquardt algorithm achieves higher position prediction accuracy while maintaining a compact network structure.

Yalavarthi and Singh²⁵² proposed an optimized artificial neural network (ANN)-based sensorless control strategy for SRMs, aiming to address the modeling challenges and position sensor dependency caused by nonlinear saturation characteristics. The core innovation lies in utilizing the Levenberg-Marquardt (LM) backpropagation learning algorithm to train the ANN model, enabling rotor position estimation through real-time flux linkage data and eliminating the dependency on pre-stored magnetic characteristic lookup tables (LUTs) inherent in traditional methods. The ANN structure adopts a single hidden layer design, optimizing the number of neurons to reduce both training and validation errors while improving position estimation accuracy. The control strategy integrates advanced angle control (AAC), achieving speed regulation by adjusting conduction and turn-off angles instead of traditional hysteresis current control to reduce switching losses.

The core methodology of this paper focuses on ANN output computation and the LM weight update mechanism. The ANN output formula is expressed as follows:

y = \sum_{j = 1}^{m} (w_{jo} x_{hj}) + b_{o}

(13)

where w_jo represents the weight vector from the hidden layer to the output layer, x_hj denotes the output of hidden layer neurons, and b_o is the output layer bias.

This formula defines the computation process of the neural network output layer, where the output directly corresponds to the estimated rotor position value. Its accuracy depends on the number of hidden layer neurons and the quality of training data. The formula demonstrates how the ANN maps nonlinear inputs to position signals without requiring an explicit magnetic characteristic model. The LM weight update formula is expressed as follows:

x (n + 1) = x (n) - {J^{T} (n) J (n) + μ I}^{- 1} J^{T} (n) e_{y} (n)

(14)

where J is a Jacobian matrix and the coefficient is μ ≥ 0.

Although the LM algorithm in equation (14) offers rapid convergence, the matrix inversion operation imposes a heavy computational burden. Deploying this neural network observer on low-cost microcontrollers (MCUs) often requires approximation or simplification, which is a critical consideration for commercial SRM drives.

This formula serves as the core of the LM algorithm, used for iteratively updating ANN weights and biases. When μ = 0, the algorithm degenerates to the Gauss-Newton method, ensuring rapid convergence; when μ is large, it approaches gradient descent, enhancing stability. This formula guarantees that ANN training achieves high precision with a mean square error (MSE) of 2 × 10⁻⁴ degrees within 1000 epochs, overcoming the local minima problem inherent in traditional stochastic gradient descent (SGD).

Figure 9 illustrates the overall control architecture of the proposed method. The block diagram integrates position estimation, speed control, and power conversion modules: the left section contains the ANN position estimator, which receives phase current and flux linkage as inputs and outputs rotor position; the central section comprises the speed control loop, where a PI regulator compares reference speed with estimated speed to generate current references; the right section features the advanced angle control (AAC) module, which calculates advance angles to derive conduction and turn-off angles, driving a split DC converter (SDC) to achieve single-pulse mode (SPM) operation and reduce switching losses. The diagram highlights the synergy between ANN and AAC: the ANN provides high-precision position feedback, enabling AAC to dynamically adjust commutation angles and ensure speed tracking accuracy. Simultaneously, the architecture supports a startup algorithm that detects initial position through diagnostic pulses, smoothly transitioning to ANN estimation mode to enhance system reliability.

Figure 9.

ANN-based control architecture.

Despite their powerful nonlinear fitting capabilities, the practical deployment of neural-network-based estimators is often constrained by the “black-box” nature, which complicates stability analysis, and the high computational burden associated with real-time matrix operations, making them less favorable than model-based observers for cost-sensitive or safety-critical applications.

Comparison of major sensorless control strategies for SRMs

To provide a clear quantitative benchmark for evaluating the diverse sensorless strategies discussed herein, Table 1 summarizes the key performance indicators across representative methodologies. This comparative overview highlights the inherent trade-offs between estimation accuracy, computational complexity, and robustness to parameter variations, offering researchers a quick reference to select appropriate algorithms based on specific application constraints

Table 1.

Quantitative performance comparison of major sensorless control strategies for SRMs.

Strategy category	Rep. methods (Ref.)	Estimation error (°)	Computational load	Robustness to saturation	Key limitation
HF pulse injection	TMSC,²¹⁹ Triple-Threshold²¹⁷	0.5–2.0	High	Low	Torque ripple, Acoustic noise
Unsaturated inductance	UIR-IVCT,²²² RPLL²²³	1.0–3.0	Medium	Very low	Fails under heavy load
Feature-point detection	D-ISNZ,²³⁶ Slope Zero-Crossing²³⁴	1.5–4.0	Low	High	Poor dynamic response
Model-based observer	QFE-PLL,²³⁹ SMO²³⁸	0.8–2.5	Medium-high	Medium	Dependent on model accuracy
Intelligent algorithm	Optimized ANN,²⁵² BPNN²⁵¹	0.5–1.5	Very high	High	Training data dependency, HW cost

Comparative perspectives: Sensorless control in other traction motors

While this review primarily focuses on SRM sensorless control, it is instructive to contextualize recent advancements within the broader family of traction motors, particularly Permanent Magnet Synchronous Motors (PMSMs) and Interior Permanent Magnet (IPM) motors, which dominate the electric vehicle market. Unlike SRMs that rely on reluctance torque and saliency-based position estimation, PMSMs often utilize permanent magnet flux for back-EMF observation. However, recent research in PMSM control shares conceptual parallels with SRM advancements. For instance, similar to the push for full-speed-range sensorless operation in SRMs, PMSM research has explored static current error elimination algorithms in predictive current control to enhance estimation robustness under low-speed conditions.²⁵³ Furthermore, the design philosophy for PMSMs has evolved toward topology-oriented multi-objective optimization using AutoML-based surrogate models to balance electromagnetic performance, efficiency, and cost.²⁵⁴ Although the physical principles differ—with SRMs favoring structural simplicity and PMSMs favoring high power density—both motor types face common challenges in terms of parameter sensitivity and real-time computational burdens for high-performance transportation electrification. This cross-domain comparison highlights that while SRMs offer distinct advantages in harsh environments due to their rugged structure, the algorithmic innovations in PMSM control provide valuable insights for further refining SRM sensorless strategies.

Conclusion and future directions

Conclusion

In the field of sensorless control for SRMs, position sensing strategies exhibit distinct technical boundaries and complementary characteristics across different speed ranges. For low-speed operation, pulse voltage injection methods extract rotor position information by applying high-frequency excitation signals and detecting current responses, offering high signal-to-noise ratios but potentially introducing additional vibration and acoustic noise. Methods based on unsaturated region flux linkage modeling utilize nonlinear mapping relationships between flux linkage and position to achieve position estimation, though their accuracy heavily depends on motor parameter precision and is susceptible to temperature drift and saturation effects. In medium to high-speed ranges, rotor-characteristic-position-based strategies enable position sensing by detecting phase inductance variations, featuring simple structures but limited dynamic performance. Position-observer-based methods achieve position tracking through constructed motor dynamic models, demonstrating good robustness but relying on model accuracy. Meanwhile, intelligent-algorithm-based approaches establish nonlinear mapping relationships through data-driven methods, showing strong adaptability in complex operating conditions but facing challenges such as high computational complexity and insufficient generalization capability. Future research should focus on hybrid position observation schemes that integrate multiple strategies, combining parameter adaptive compensation and intelligent optimization algorithms to achieve high-precision, high-reliability sensorless control across the entire speed range. This direction will simultaneously enhance system disturbance resistance and dynamic response performance.

Future directions

While hybrid strategies remain a promising avenue, future research must transcend simple algorithmic stacking and address the fundamental bottlenecks of SRM sensorless control with technically specific solutions. We identify three critical directions for future investigation:

Physics-informed neural networks (PINNs) for magnetic characteristic-free modeling

Instead of purely data-driven black-box neural networks, future work should focus on PINNs that embed the Maxwell equations and inductance saturation constraints into the loss function. This approach promises to eliminate the reliance on offline 3D-LUT measurements while ensuring physical consistency, directly tackling the core weakness of current model-based methods.

Cross-domain robustness synthesis with PMSM control techniques

Drawing insights from recent advancements in PMSM control, future SRM observers should incorporate parameter self-calibration mechanisms. Specifically, the concept of AutoML-based surrogate modeling, successfully applied in IPM topology optimization, should be adapted to optimize the hyperparameters of sliding mode observers or PLLs in SRMs, enhancing robustness against temperature drift and manufacturing tolerances.

Edge-AI implementation and mixed-signal analog/digital architectures

To overcome the high computational burden of intelligent algorithms, future research should explore mixed-signal hardware implementations. Deploying critical nonlinear estimation tasks on dedicated analog circuits while reserving digital cores for logic control can drastically reduce latency and power consumption, making high-precision sensorless control viable for cost-sensitive applications.

These directions shift the focus from merely improving estimation accuracy to enhancing the system-level viability and parameter autonomy of SRM drives in harsh electrified transportation environments.

Footnotes

Handling Editor: Aarthy Esakkiappan

ORCID iD

Xiaodong Sun

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was Funded by Sci&Tech Program of Huai’an (HAG202302) and Major Projects of the Basic Science (Natural Science) Research Program in Jiangsu Higher Education Institutions (24KJA470002).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Chaurasiya

Bhattacharya

Das

. Reduced switch multilevel converter for grid fed SRM drive to improve magnetization and demagnetization characteristics of an SRM. IEEE Trans Ind Appl 2023; 59(6): 6804–6816. https://doi.org/10.1109/tia.2023.3305594

Ali

Dominic

Prabhakaran

. A systematic approach to digital control development for four-phase SRM drive using single current sensor for medium power applications. IEEE Access 2024; 12: 34074–34088. https://doi.org/10.1109/access.2024.3372988

Zhang

Xia

Zheng

, et al. Permanent magnet synchronous motor drive system for agricultural equipment: a review. Agriculture 2025; 15(19): 2007. https://doi.org/10.3390/agriculture15192007

Shi

Liu

, et al. Design and experiment of planting mechanism of automatic transplanter for densely planted vegetables. Agriculture 2024; 14(8): Article no. 1357.

Rana

Teja

AVR

. Fast discharging (N+1) switch converter with regenerative flyback operation for N-phase SRM drives. IEEE Trans Power Electron 2022; 37(7): 8359–8368. https://doi.org/10.1109/tpel.2022.3150229

Singh

Gupta

. Totem pole dual output bridgeless rectifier for high-performance low power SRM drive. IEEE Trans Power Electron 2025; 40(11): 16498–16511. https://doi.org/10.1109/tpel.2025.3589251

Mihic

Terzic

Vukosavic

. A new nonlinear analytical model of the SRM with included multiphase coupling. IEEE Trans Energy Convers 2017; 32(4): 1322–1334. https://doi.org/10.1109/tec.2017.2707587

Ali

Dominic

Prabhakaran

, et al. A bidirectional interleaved totem pole PFC-based integrated on-board charger for EV SRM drive. IEEE Access 2024; 12: 101909–101920. https://doi.org/10.1109/access.2024.3432791

Ali

Narimani

. Fault-tolerant SRM drives—a review. IEEE Trans Power Electron 2024; 39(8): 10261–10275. https://doi.org/10.1109/tpel.2024.3392713

10.

Chen

Wang

, et al. Modeling and test on height adjustment system of electrically-controlled air suspension for agricultural vehicles. Int J Agric Biol Eng 2016; 9(2): 40–47. https://doi.org/10.3965/j.ijabe.20160902.1852

11.

Liu

Amoah

, et al. Control system of a performance test-bed for frost protection wind machines. Int J Agric Biol Eng 2016; 9(6): 36–43. https://doi.org/10.3965/j.ijabe.20160906.1664

12.

Cui

Xue

, et al. Design and experiment of electro hydraulic active suspension for controlling the rolling motion of spray boom. Int J Agric Biol Eng 2019; 12(4): 72–81. https://doi.org/10.25165/j.ijabe.20191204.4648

13.

Zhu

Zhang

, et al. Design of an electronically controlled fertilization system for an air-assisted side-deep fertilization machine. Agriculture 2023; 13(12): 2210. https://doi.org/10.3390/agriculture13122210

14.

Han

Yan

Tang

. Method of controlling tillage depth for agricultural tractors considering engine load characteristics. Biosyst Eng 2023; 227: 95–106. https://doi.org/10.1016/j.biosystemseng.2023.01.011

15.

Yao

Liu

, et al. Precise servo-control system of a dual-axis positioning tray conveying device for automatic transplanting machine. Agriculture 2024; 14(8): 1431. https://doi.org/10.3390/agriculture14081431

16.

Yang

Zhai

Gao

, et al. Planting uniformity performance of motor-driven maize precision seeding systems. Int J Agric Biol Eng 2022; 15(5): 101–108. https://doi.org/10.25165/j.ijabe.20221505.5911

17.

Huang

Wang

, et al. Design method and experiment of machinery for combined application of seed, fertilizer and herbicide. Int J Agric Biol Eng 2019; 12(4): 63–71. https://doi.org/10.25165/j.ijabe.20191204.4679

18.

Ahmad

Adeel

Qiu

, et al. Sowing uniformity of bed-type pneumatic maize planter at various seedbed preparation levels and machine travel speeds. Int J Agric Biol Eng 2021; 14(1): 165–171. https://doi.org/10.25165/j.ijabe.20211401.5054

19.

Zhang

Chen

Teng

, et al. Model construction for field operation machinery selection and configuration in wheat-maize double cropping system. Int J Agric Biol Eng 2021; 14(3): 82–89. https://doi.org/10.25165/j.ijabe.20211404.6383

20.

Shokrian

Wang

. Evaluation of three-phase centrifugal separator machine (Tricanter) for olive oil extraction. Czech J Food Sci 2022; 40(3): 221–228. https://doi.org/10.17221/278/2021-cjfs

21.

Yuan

Feng

. Robots and autonomous machines for sustainable agriculture production. Agriculture 2023; 13(7): 1340. https://doi.org/10.3390/agriculture13071340

22.

Xiaoxue

Yana

, et al. Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery. Int J Agric Biol Eng 2024; 17(2): 27–36. https://doi.org/10.25165/j.ijabe.20241702.8127

23.

Yue

Yao

Zhang

, et al. Design and experiment of dual-row seedling pick-up device for high-speed automatic transplanting machine. Agriculture 2024; 14(6): 942. https://doi.org/10.3390/agriculture14060942

24.

Jia

Tai

Wang

, et al. Design and simulation of intra-row obstacle avoidance shovel-type weeding machine in orchard. Agriculture 2024; 14(7): 1124.

25.

Zhu

Yang

Wang

, et al. Energy saving performance of agricultural tractor equipped with mechanic-electronic-hydraulic powertrain system. Agriculture 2022; 12(3): 436. https://doi.org/10.3390/agriculture12030436

26.

Sun

Wen

Zhu

, et al. Fcs-mpc with improved prediction for suppressing torque and current pulsations of switched reluctance motors. IEEE Trans Ind Electron 2025; 72(7): 6831–6839. https://doi.org/10.1109/tie.2024.3515281

27.

Aiso

Akatsu

. High speed SRM using vector control for electric vehicle. CES Trans Elect Mach Syst 2020; 4(1): 61–68. https://doi.org/10.30941/cestems.2020.00009

28.

Mishra

Singh

. Grid-integrated SRM-driven solar water pump with power flow management. IEEE J Emerg Sel Top Power Electron 2021; 9(3): 2723–2734. https://doi.org/10.1109/jestpe.2020.3022835

29.

Sun

Garcia

, et al. Model predictive current control of six-phase switched reluctance motor with enhanced robustness based on improved lehuy model. IEEE Trans Transp Electrification 2025; 11(2): 6177–6187. https://doi.org/10.1109/tte.2024.3500363

30.

Mishra

Singh

. An improved control technique for grid interactive 4-phase SRM driven solar powered WPS using three-level boost converter. IEEE Trans Ind Inform 2021; 17(1): 290–299. https://doi.org/10.1109/tii.2019.2957498

31.

Malekpour

Rashidi

Nejad

SMS

, et al. Multi-level voltage high fault-tolerance modular SRM converter with simultaneous charge and discharge capability for HEV. IEEE Access 2025; 13: 127447–127461. https://doi.org/10.1109/ACCESS.2025.3587508

32.

Singh

. A bridgeless rectifier with improved power quality for low power SRM drive. IEEE Trans Ind Appl 2025; 61(3): 4040–4053. https://doi.org/10.1109/tia.2025.3540998

33.

Feng

Sun

Bramerdorfer

, et al. A review on control techniques of switched reluctance motors for performance improvement. Renew Sustain Energ Rev 2024; 199: 114454. https://doi.org/10.1016/j.rser.2024.114454

34.

Yalavarthi

Singh

. An adaptive-gain super-twisting position observer for grid-interfaced SRM water pump. IEEE Trans Consum Electron 2022; 68(4): 366–375. https://doi.org/10.1109/tce.2022.3201707

35.

Chithrabhanu

Vasudevan

. Quantification of noise benefits in torque control strategies of SRM drives. IEEE Trans Energy Convers 2023; 38(1): 585–598. https://doi.org/10.1109/tec.2022.3201325

36.

Sun

Zhu

Cai

, et al. Optimized-sector-based model predictive torque control with sliding mode controller for switched reluctance motor. IEEE Trans Energy Convers 2024; 39(1): 379–388. https://doi.org/10.1109/tec.2023.3301000

37.

Cabezuelo

Kortabarria

Andreu

, et al. Synchronized switching modulation to reduce the DC-link current in SRM drives. IEEE Access 2020; 8: 57849–57858. https://doi.org/10.1109/access.2020.2982269

38.

De Paula

Williamson

Barros

TADS

. Four-quadrant model following sliding mode cruise control for SRM with DITC applied to transportation electrification. IEEE Trans Transp Electrification 2022; 8(3): 3090–3099. https://doi.org/10.1109/tte.2022.3162405

39.

Feng

Sun

Guo

, et al. Advanced torque sharing function strategy with sliding mode control for switched reluctance motors. IEEE Trans Transp Electrification 2024; 10(1): 2302–2311. https://doi.org/10.1109/tte.2023.3287159

40.

Siddique

Singh

Shastri

. RSM assisted MOGA for SRM EV drive control factors optimization. IEEE Trans Ind Appl 2024; 60(2): 3200–3209. https://doi.org/10.1109/tia.2023.3339711

41.

Rayeen

Azeem

Iqbal

, et al. An improved AC supply-based method for measuring the inductance profile of SRM using SOGI. IEEE Trans Instrum Meas 2025; 74: 1–11. https://doi.org/10.1109/tim.2024.3502778

42.

Sun

Zhu

Cai

, et al. Current fault tolerance control strategy for 3-phase switched reluctance motor combined with position signal reconstruction. IEEE Trans Energy Convers 2023; 38(3): 1679–1690. https://doi.org/10.1109/tec.2023.3239525

43.

Bhaktha

Jose

Vamshik

, et al. Driving cycle-based design optimization and experimental verification of a switched reluctance motor for an E-rickshaw. IEEE Trans Transp Electrification 2024; 10(4): 9959–9974. https://doi.org/10.1109/tte.2024.3370401

44.

Djelloul-Khedda

Boughrara

Dubas

, et al. Nonlinear analytical prediction of magnetic field and electromagnetic performances in switched reluctance machines. IEEE Trans Magn 2017; 53(7): 1–11. https://doi.org/10.1109/tmag.2017.2679686

45.

Feng

Sun

Yang

, et al. Optimal torque sharing function control for switched reluctance motors based on active disturbance rejection controller. IEEE/ASME Trans Mechatron 2023; 28(5): 2600–2608. https://doi.org/10.1109/tmech.2023.3240986

46.

Anvari

Toliyat

Fahimi

. Simultaneous optimization of geometry and firing angles for in-wheel switched reluctance motor drive. IEEE Trans Transp Electrification 2018; 4(1): 322–329. https://doi.org/10.1109/tte.2017.2766452

47.

Sun

Gan

, et al. Cascaded multiport converter for SRM-based hybrid electrical vehicle applications. IEEE Trans Power Electron 2019; 34(12): 11940–11951. https://doi.org/10.1109/tpel.2019.2909187

48.

Gouda

Salah

. A proposed design, implementation and control of doubly fed switched reluctance motor. CES Trans Elect Mach Syst 2019; 3(1): 101–106. https://doi.org/10.30941/cestems.2019.00014

49.

Diao

Sun

Bramerdorfer

, et al. Design optimization of switched reluctance machines for performance and reliability enhancements: a review. Renew Sustain Energ Rev 2022; 168(10): 112785. https://doi.org/10.1016/j.rser.2022.112785

50.

Liu

Kim

M-J

, et al. Characteristics analysis and comparison of conventional and segmental rotor type 12/8 switched reluctance motors. IEEE Trans Ind Appl 2019; 55(3): 3129–3137. https://doi.org/10.1109/tia.2018.2859324

51.

Liu

Cao

Cai

. A magnetic bearing switched reluctance motor with simultaneous excitation by a modified half-bridge converter. IEEE Trans Magn 2019; 55(10): 1–5. https://doi.org/10.1109/tmag.2019.2921725

52.

Sun

Xiong

Yao

, et al. A hybrid control strategy for multimode switched reluctance motors. IEEE/ASME Trans Mechatron 2022; 27(6): 5605–5614. https://doi.org/10.1109/tmech.2022.3186383

53.

Shang

Chen

, et al. A new control method based on DTC and MPC to reduce torque ripple in SRM. IEEE Access 2019; 7: 68584–68593. https://doi.org/10.1109/access.2019.2917317

54.

Wang

Yang

, et al. Vector control strategy of a T-type three-level converter driving a switched reluctance motor. Chin J Elect Eng 2019; 5(4): 15–21. https://doi.org/10.23919/cjee.2019.000023

55.

Sun

Xiong

Yao

, et al. A unified control method combined with improved TSF and LADRC for SRMs using modified grey wolf optimization algorithm. ISA Trans 2022; 131: 662–671. https://doi.org/10.1016/j.isatra.2022.05.013

56.

, et al. Modular tri-port converter for switched reluctance motor based hybrid electrical vehicles. IEEE Access 2019; 7: 15989–15998. https://doi.org/10.1109/access.2019.2894818

57.

Jiang

Chau

Liu

, et al. Design and analysis of wireless switched reluctance motor drives. IEEE Trans Ind Electron 2019; 66(1): 245–254. https://doi.org/10.1109/tie.2018.2829684

58.

Gan

Chen

, et al. An overview of fault-diagnosis and fault-tolerance techniques for switched reluctance machine systems. IEEE Access 2019; 7: 174822–174838. https://doi.org/10.1109/ACCESS.2019.2956552

59.

Diao

Bramerdorfer

Sun

, et al. Multiobjective design optimization of a novel switched reluctance motor with unequal alternating stator yoke segments. IEEE Trans Transp Electrification 2023; 9(1): 512–521. https://doi.org/10.1109/tte.2022.3174337

60.

Anand

Singh

. Modified dual output cuk converter-fed switched reluctance motor drive with power factor correction. IEEE Trans Power Electron 2019; 34(1): 624–635. https://doi.org/10.1109/tpel.2018.2827048

61.

Wang

, et al. Development and operation control of a switched-reluctance motor driven flywheel. IEEE Trans Power Electron 2019; 34(1): 526–537. https://doi.org/10.1109/tpel.2018.2814790

62.

Yuan

Yang

, et al. Open-circuit fault-tolerant control strategy based on five-level power converter for SRM system. CES Trans Elect Mach Syst 2019; 3(2): 178–186. https://doi.org/10.30941/cestems.2019.00024

63.

Sun

Xiong

Yao

, et al. High fault-tolerance evaluation on position signal for switched reluctance motor drives. IEEE Trans Energy Convers 2022; 37(3): 1844–1853. https://doi.org/10.1109/tec.2022.3161701

64.

Zhang

Habetler

, et al. Modeling, design optimization, and applications of switched reluctance machines—a review. IEEE Trans Ind Appl 2019; 55(3): 2660–2681. https://doi.org/10.1109/tia.2019.2897965

65.

Wang

Ding

. Performance evaluation on a novel power converter with minimum number of switches for a six-phase switched reluctance motor. IEEE Trans Ind Electron 2019; 66(3): 1693–1702. https://doi.org/10.1109/tie.2018.2840480

66.

Sun

Pan

, et al. Sensorless control strategy for SRM based on flux linkage in medium to high-speed range. IEEE Trans Ind Electron 2025; 72(10): 9922–9930. https://doi.org/10.1109/tie.2025.3553166

67.

Sun

, et al. Electromagnetic analysis on novel rotor-segmented axial-field SRM based on dynamic magnetic equivalent circuit. IEEE Trans Magn 2019; 55(6): 1–5. https://doi.org/10.1109/tmag.2019.2901002

68.

Ding

Yang

. Performance improvement for segmented-stator hybrid-excitation SRM drives using an improved asymmetric half-bridge converter. IEEE Trans Ind Electron 2019; 66(2): 898–909. https://doi.org/10.1109/tie.2018.2833034

69.

Gan

Sun

, et al. MMC-based SRM drives with decentralized battery energy storage system for hybrid electric vehicles. IEEE Trans Power Electron 2019; 34(3): 2608–2621. https://doi.org/10.1109/tpel.2018.2846622

70.

Deepak

Janaki

Bharatiraja

. Optimize SRM rotor geometry analysis for 3kW light electric vehicle grade performances. In: Proceedings of the 2022 international interdisciplinary conference on mathematics, engineering and science (MESIICON 2022), Durgapur, India, 2022, pp.1–6.

71.

Deng

Mecrow

, et al. Cost-effective and high-efficiency variable-speed switched reluctance drives with ring-connected winding configuration. IEEE Trans Energy Convers 2019; 34(1): 120–129. https://doi.org/10.1109/tec.2018.2851065

72.

Singh

Kumar

Singh

. Position sensorless SRM drive using SMO for PV integrated battery powered light electric vehicle with regenerative braking capability. IEEE J Emerg Sel Topics Ind Electron 2025; 6(2): 585–593. https://doi.org/10.1109/jestie.2024.3467032

73.

Yalavarthi

Singh

. Smo-based position sensorless SRM drive for battery-supported PV submersible pumps. IEEE J Emerg Sel Top Power Electron 2022; 10(4): 3917–3926. https://doi.org/10.1109/jestpe.2021.3067955

74.

Sun

Wang

Yao

, et al. Position sensorless control of switched reluctance motors based on angle adjustment using nonlinear inductance and flux model. IEEE Trans Ind Electron 2024; 71(12): 15467–15477. https://doi.org/10.1109/tie.2024.3393123

75.

Gundogmus

Das

Yasa

, et al. Acoustic noise mitigation in high pole count switched reluctance machines utilizing skewing method on stator and rotor poles. IEEE Trans Ind Electron 2022; 69(6): 5581–5593. https://doi.org/10.1109/TIE.2021.3095808

76.

Patel

Shah

Lokhande

. A novel approach to the design and development of 12/15 radial field C-core switched reluctance motor for implementation in electric vehicle application. IEEE Trans Vehicular Technol 2018; 67(9): 8031–8040. https://doi.org/10.1109/tvt.2018.2839695

77.

Furqani

Wiguna

Chiba

, et al. Experimental verification of acoustic noise and radial force sum variation in switched reluctance motor. IEEE Trans Ind Appl 2021; 57(3): 2481–2493. https://doi.org/10.1109/tia.2021.3066955

78.

Sun

Wang

Cao

, et al. Nonlinear fast modeling method of flux linkage and torque for a 12/8 switched reluctance motors. IEEE Trans Power Electron 2024; 39(5): 5298–5309. https://doi.org/10.1109/tpel.2024.3363027

79.

Yasa

Sozer

Garip

. Acoustic noise mitigation of switched reluctance machines with leaf springs. IEEE Trans Ind Electron 2023; 70(2): 1250–1260. https://doi.org/10.1109/tie.2022.3159969

80.

Kumar

Singh

. Modified direct torque control of solar fed sensorless switched reluctance motor drive for electric vehicle with regenerative braking. IEEE Trans Ind Appl 2024; 60(2): 3155–3164. https://doi.org/10.1109/tia.2023.3330452

81.

Hekmati

Brown

. Rotary-reciprocating movement switched-reluctance machines with consequent axially shifted poles. IEEE Trans Magn 2018; 54(5): 1–10. https://doi.org/10.1109/tmag.2018.2803156

82.

Sun

Zhu

Cai

, et al. Speed sensorless control of switched reluctance motors in full-speed range based on inductance characteristics. IEEE Trans Transp Electrification 2024; 10(2): 4018–4028. https://doi.org/10.1109/tte.2023.3306876

83.

Roy

Sengupta

. An experimentally validated novel analytical approach for modeling of a saturated switched reluctance motor. IEEE Trans Magn 2022; 58(12): 1–11. https://doi.org/10.1109/tmag.2022.3210155

84.

Juarez-Leon

Bilgin

. A high-efficiency silicon-carbide bidirectional industrial switched reluctance motor drive. IEEE Access 2025; 13: 126513–126524. https://doi.org/10.1109/access.2025.3590302

85.

Vadamodala

Das

Chowdhury

, et al. A modified geometry-based analytical model of switched reluctance motor for rapid design process. IEEE Trans Ind Appl 2025; 61(3): 3753–3763. https://doi.org/10.1109/tia.2025.3540994

86.

Sun

Tang

Tian

, et al. Position sensorless control of switched reluctance motor drives based on a new sliding mode observer using Fourier flux linkage model. IEEE Trans Energy Convers 2022; 37(2): 978–988. https://doi.org/10.1109/tec.2021.3125494

87.

Rahnamaei

Nejad

SMS

Rashidi

, et al. Dynamic thermal model for winding temperature of an SRM in an integrated battery charger utilized in electric vehicles. IEEE Trans Energy Convers 2021; 36(3): 1766–1775. https://doi.org/10.1109/tec.2020.3045883

88.

Amaral

Fernão Pires

Foito

, et al. A fault detection and diagnosis method based on the currents entropy indexes for the SRM drive with a fault tolerant multilevel converter. IEEE Trans Ind Appl 2023; 60(1): 1–13. https://doi.org/10.1109/tia.2023.3328295

89.

Malekipour

Nejad

SMS

Rashidi

. Vibration reduction of SRM through controlling the average of applied demagnetization voltage. IEEE Trans Energy Convers 2021; 36(3): 1756–1765. https://doi.org/10.1109/tec.2020.3038443

90.

Sun

Tang

Tian

, et al. Sensorless control with fault-tolerant ability for switched reluctance motors. IEEE Trans Energy Convers 2022; 37(2): 1272–1281. https://doi.org/10.1109/TEC.2021.3119928

91.

Al Eit

Dular

Bouillault

, et al. Perturbation finite element method for efficient copper losses calculation in switched reluctance machines. IEEE Trans Magn 2017; 53(6): 1–4. https://doi.org/10.1109/tmag.2017.2655339

92.

Kim

J-H

Kim

R-Y

. Sensorless direct torque control using the inductance inflection point for a switched reluctance motor. IEEE Trans Ind Electron 2018; 65(12): 9336–9345. https://doi.org/10.1109/tie.2018.2821632

93.

Rana

Teja

AVR

. A mathematical torque ripple minimization technique based on a nonlinear modulating factor for switched reluctance motor drives. IEEE Trans Ind Electron 2022; 69(2): 1356–1366. https://doi.org/10.1109/tie.2021.3063871

94.

Chai

Zuo

Zhu

, et al. Optimal excitation angles analysis based on the SRM quasi-linear normalized model for enhanced overall performance. IEEE Trans Power Electron 2025; 40(11): 16431–16442. https://doi.org/10.1109/tpel.2025.3587294

95.

Sun

Xie

Liu

, et al. Multiport PV-assisted electric-drive-reconstructed bidirectional charger with G2V and V2G/V2L functions for SRM drive-based EV application. IEEE J Emerg Sel Top Power Electron 2023; 11(3): 3398–3408. https://doi.org/10.1109/jestpe.2023.3240434

96.

Tang

Wang

, et al. A drive topology for high-speed SRM with bidirectional energy flow and fast demagnetization voltage. IEEE Trans Ind Electron 2021; 68(10): 9242–9253. https://doi.org/10.1109/tie.2020.3022497

97.

Cheng

Wang

, et al. An integrated electrified powertrain topology with SRG and SRM for plug-in hybrid electrical vehicle. IEEE Trans Ind Electron 2020; 67(10): 8231–8241. https://doi.org/10.1109/tie.2019.2947854

98.

Han

Hong

, et al. Speed synchronization control of dual-SRM drive with ISMC-based cross-coupling control strategy. IEEE Trans Transp Electrification 2023; 9(2): 2524–2534. https://doi.org/10.1109/tte.2022.3208230

99.

Zhang

Song

Wang

, et al. High-resistance connection fault diagnosis of SRM based on multisensor calibrated transformer with shifted windows. IEEE Sens J 2023; 23(24): 30971–30983. https://doi.org/10.1109/jsen.2023.3331695

100.

Wei

Zhao

Liu

, et al. Speed control for SRM drive system based on switching variable proportional desaturation PI regulator. IEEE Access 2021; 9: 69735–69746. https://doi.org/10.1109/access.2021.3078194

101.

Song

Han

, et al. Multisource deep feature fusion of optimized symmetrized dot patterns for SRM fault diagnosis. IEEE Trans Instrum Meas 2024; 73: 1–13. https://doi.org/10.1109/tim.2024.3396854

102.

Han

Hong

Chen

, et al. An improved virtual-shaft control strategy for speed synchronization of dual-SRM drive. IEEE Trans Ind Electron 2024; 71(6): 5485–5495. https://doi.org/10.1109/tie.2023.3294543

103.

Cai

Liu

Song

. Phase current reconstruction and suppression in a single current sensor measurement-based four-phase SRM-integrated drive system. IEEE Trans Instrum Meas 2025; 74: 1–9. https://doi.org/10.1109/tim.2025.3544701

104.

Cheng

Jia

, et al. Improved speed synchronous strategy for dual-SRM drive with feedforward compensation. IEEE Trans Transp Electrification 2025; 11(2): 7131–7141. https://doi.org/10.1109/tte.2024.3524546

105.

Han

Zhang

, et al. Fighting fake news: two stream network for deepfake detection via learnable SRM. IEEE Trans Biom Behav Identity Sci 2021; 3(3): 320–331. https://doi.org/10.1109/tbiom.2021.3065735

106.

Ahmad

Narayanan

. Predictive control based constant current injection scheme for characterization of switched reluctance machine. IEEE Trans Ind Appl 2018; 54(4): 3383–3392. https://doi.org/10.1109/tia.2018.2818646

107.

Wiguna

Cai

Lilian

LLSS

, et al. Vibration and acoustic noise reduction in switched reluctance motor by selective radial force harmonics reduction. IEEE Open J Ind Appl 2023; 4: 23–34. https://doi.org/10.1109/ojia.2022.3229849

108.

Mehta

Kabir

Pramod

, et al. Segmented rotor mutually coupled switched reluctance machine for low torque ripple applications. IEEE Trans Ind Appl 2021; 57(4): 3582–3594. https://doi.org/10.1109/tia.2021.3073384

109.

Shah

Payami

. An integrated driving/charging four-phase switched reluctance motor drive with reduced current sensors for electric vehicle application. IEEE J Emerg Sel Top Power Electron 2022; 10(6): 6880–6890. https://doi.org/10.1109/JESTPE.2021.3120468

110.

Abd Elmutalab

Elrayyah

Husain

, et al. Extending the speed range of a switched reluctance motor using a fast demagnetizing technique. IEEE Trans Ind Appl 2018; 54(4): 3294–3304. https://doi.org/10.1109/tia.2018.2828390

111.

Sun

Xiong

Yang

, et al. Torque ripple reduction for a 12/8 switched reluctance motor based on a novel sliding mode control strategy. IEEE Trans Transp Electrification 2023; 9(1): 359–369. https://doi.org/10.1109/tte.2022.3161078

112.

Cheng

Wang

Yang

, et al. An integrated SRM powertrain topology for plug-in hybrid electric vehicles with multiple driving and onboard charging capabilities. IEEE Trans Transp Electrification 2020; 6(2): 578–591. https://doi.org/10.1109/tte.2020.2987167

113.

M-Z

Guo

Z-W

Liaw

C-M

. A battery/supercapacitor hybrid powered EV SRM drive and microgrid incorporated operations. IEEE Trans Transp Electrification 2021; 7(4): 2848–2863. https://doi.org/10.1109/tte.2021.3081103

114.

Sun

Chen

Liu

, et al. Quasi-Z-source-fed SRM drive for torque ripple minimization and speed range extension with three-switch conduction. IEEE Trans Ind Electron 2023; 70(12): 11923–11933. https://doi.org/10.1109/tie.2022.3232653

115.

Feng

Sun

Tian

, et al. Direct torque control with variable flux for an SRM based on hybrid optimization algorithm. IEEE Trans Power Electron 2022; 37(6): 6688–6697. https://doi.org/10.1109/tpel.2022.3145873

116.

Sun

Zhang

Liu

, et al. Variable-winding-based highly integrated electric-drive-reconstructed system for SRM-based EVS. IEEE Trans Power Electron 2024; 39(6): 7458–7467. https://doi.org/10.1109/tpel.2024.3367939

117.

, et al. A novel power and signal composite modulation approach to powerline data communication for SRM in distributed power grids. IEEE Trans Power Electron 2021; 36(9): 10436–10446. https://doi.org/10.1109/tpel.2021.3061469

118.

Chen

Fang

Guan

, et al. Fault diagnosis for power converter in SRM drives based on current prediction. IEEE Trans Ind Electron 2022; 69(12): 13576–13585. https://doi.org/10.1109/tie.2021.3137607

119.

Diao

Sun

Yao

. Robust-oriented optimization of switched reluctance motors considering manufacturing fluctuation. IEEE Trans Transp Electrification 2022; 8(2): 2853–2861. https://doi.org/10.1109/tte.2021.3137385

120.

Zan

Jiang

, et al. Modular battery management for SRM drives in hybrid vehicles based on a novel modular converter. IEEE Access 2020; 8: 136296–136306. https://doi.org/10.1109/access.2020.3011451

121.

Shao

Chai

. Model-adaptive feedforward structure for enhanced dynamic response in SRM current controller based on operating point transformation. IEEE Trans Power Electron 2024; 39(11): 15001–15010. https://doi.org/10.1109/tpel.2024.3437638

122.

Han

Zhang

Wang

, et al. Variable switching point model-free predictive current control strategy for srm. IEEE Trans Ind Electron 2025; 72(5): 4470–4480. https://doi.org/10.1109/tie.2024.3463025

123.

Sun

Feng

Zhu

, et al. Optimal design of terminal sliding mode controller for direct torque control of SRMs. IEEE Trans Transp Electrification 2022; 8(1): 1445–1453. https://doi.org/10.1109/tte.2021.3111889

124.

Liang

Callegaro

Howey

, et al. Analytical calculation of temporal and circumferential orders of radial force density harmonics in external-rotor and internal-rotor switched reluctance machines. IEEE Open J Ind Appl 2021; 2: 70–81. https://doi.org/10.1109/ojia.2021.3071157

125.

Cui

Sun

Gan

, et al. Optimal design of saturated switched reluctance machine for low speed electric vehicles by subset quasi-orthogonal algorithm. IEEE Access 2019; 7: 101086–101095. https://doi.org/10.1109/access.2019.2929103

126.

Long

. Adaptive position control strategy of SRM-based EMA system for precision position tracking. IEEE Trans Transp Electrification 2023; 9(3): 4680–4691. https://doi.org/10.1109/tte.2023.3243107

127.

Xiao

Shi

Liu

, et al. Design of an embedded rapier loom controller and a control strategy based on srm. IEEE Access 2022; 10: 21914–21928. https://doi.org/10.1109/access.2022.3153066

128.

Tang

Xie

, et al. Single-phase H-bridge rectifier fed high-speed SRM system based on integrated power control. IEEE Trans Energy Convers 2023; 38(1): 519–529. https://doi.org/10.1109/tec.2022.3203626

129.

Fan

Lee

. Design consideration to achieve wide-speed-range operation in a switched reluctance motor. Can J Electr Comput Eng 2020; 43(4): 290–294. https://doi.org/10.1109/cjece.2020.2978265

130.

Wiguna

Furqani

Chiba

. Improved current profile selection for noise reduction of switched reluctance motor at middle speed considering back EMF. IEEE Trans Ind Appl 2021; 57(5): 4707–4719. https://doi.org/10.1109/tia.2021.3091085

131.

Sun

Wen

Han

, et al. Fault diagnosis of power converter for switched reluctance motors based on current reconstruction scheme. IEEE Trans Transp Electrification 2025; 11(1): 4169–4178. https://doi.org/10.1109/tte.2024.3455259

132.

Rana

Ravi Teja

. A fault-tolerant power converter with multi-switch fault diagnosis and repair capability for 4-phase 8/6 SRM drives. IEEE Trans Transp Electrification 2022; 8(3): 3896–3906. https://doi.org/10.1109/tte.2022.3161090

133.

Ali

Wang

Gao

, et al. Diagnosis of multicomponent faults in SRM drives based on auxiliary current reconstruction under soft-switching operation. IEEE Trans Ind Electron 2024; 71(3): 2265–2276. https://doi.org/10.1109/tie.2023.3266596

134.

Ali

Wang

Gao

, et al. Fast detection of power transistor faults in SRM drives based on transient pulse injection. IEEE J Emerg Sel Top Power Electron 2023; 11(5): 5256–5267. https://doi.org/10.1109/jestpe.2023.3293002

135.

Chithrabhanu

Vasudevan

. Analysis and design considerations of a buck-boost energy recovery-based power converter for SRM drive. IEEE J Emerg Sel Top Power Electron 2023; 11(1): 886–900. https://doi.org/10.1109/jestpe.2022.3206338

136.

Abdel-Aziz

Ahmed

Wang

, et al. A neutral-point diode-clamped converter with inherent voltage-boosting for a four-phase SRM drive. IEEE Trans Ind Electron 2020; 67(7): 5313–5324. https://doi.org/10.1109/tie.2019.2931268

137.

Sun

Wen

Zhang

, et al. Fault detection and fault-tolerant control of switched reluctance motor based on dual-sensor current detection scheme. IEEE Trans Transp Electrification 2025; 11(1): 19–28. https://doi.org/10.1109/tte.2024.3386151

138.

Shah

Payami

. Fully integrated multilevel power converter for SRM drive with charging capabilities (G2V) for electric vehicle application. IEEE J. Emerg Sel Topics Ind Electron 2023; 4(1): 198–208. https://doi.org/10.1109/jestie.2022.3190794

139.

Ali

Wang

Gao

, et al. Online diagnosis of multiple-switch simultaneous faults in SRM drives based on current errors. IEEE J Emerg Sel Top Power Electron 2024; 12(5): 5215–5224. https://doi.org/10.1109/jestpe.2024.3432641

140.

Kumawat

Shah

Payami

. A universal-input on-board charger integrated power converter for switched reluctance motor drive based EV application. IEEE Trans Ind Appl 2024; 60(4): 6458–6468. https://doi.org/10.1109/tia.2024.3397952

141.

Reddy

Parthiban

. Fault–tolerant operation of switched reluctance motor using cascaded current and PWM control with effect of commutation angle variation. IEEE Trans Ind Appl 2024; 60(6): 8807–8820. https://doi.org/10.1109/tia.2024.3429074

142.

Fan

Lee

. A concept of accelerating the demagnetization of switched reluctance motor. Can J Electr Comput Eng 2020; 43(4): 326–330. https://doi.org/10.1109/cjece.2020.3002243

143.

Han

Chen

. Improved power converter of SRM drive for electric vehicle with self-balanced capacitor voltages. IEEE Trans Transp Electrification 2021; 7(3): 1339–1348. https://doi.org/10.1109/tte.2020.3037111

144.

Yang

Cai

Zhou

, et al. The optimization of topological mechanism and dimension design of parallel transplanting machine in greenhouse. AMA-Agric Mech Asia Afr Lat Am 2021; 52(1): 61–66.

145.

Wang

Liu

. Enhanced defect detection in RF circuits using DM-SRM and elastic regularization techniques. IEEE Trans Microw Theory Tech 2024; 72(9): 5134–5142. https://doi.org/10.1109/tmtt.2024.3365613

146.

Liu

Sun

, et al. SRM-Net: joint sampling and reconstruction and mapping network for accelerated 3T brain multi-parametric MR imaging. IEEE Trans Biomed Eng 2025; 72(6): 1811–1824. https://doi.org/10.1109/tbme.2024.3523480

147.

Fang

Xiao

, et al. Low-ripple continuous control set model predictive torque control for switched reluctance machines based on equivalent linear SRM model. IEEE Trans Ind Electron 2022; 69(12): 12480–12495. https://doi.org/10.1109/tie.2021.3130344

148.

Han

Zhu

. Virtual current coefficients based power transistors fault diagnosis for small power EV-SRM drives. IEEE Trans Transp Electrification 2021; 7(4): 2881–2891. https://doi.org/10.1109/tte.2021.3082146

149.

Pei

Shao

, et al. State-complementary switching pattern-based modulation for current-ripple reduction in SRM drives using double-updating scheme. IEEE Trans Power Electron 2024; 39(3): 3498–3508. https://doi.org/10.1109/tpel.2023.3336012

150.

Guo

Gong

, et al. Model predictive torque control of switched reluctance machines with torque sharing function and PWM control signals based on linear polynomial fitting. IEEE Trans Power Electron 2025; 40(1): 40–45. https://doi.org/10.1109/tpel.2024.3444891

151.

Zhang

Yang

, et al. A switched reluctance motor torque ripple reduction strategy with deadbeat current control and active thermal management. IEEE Trans Vehicular Technol 2020; 69(1): 317–327. https://doi.org/10.1109/tvt.2019.2955218

152.

Husain

Sozer

Husain

. Dc-assisted bipolar switched reluctance machine. IEEE Trans Ind Appl 2017; 53(3): 2098–2109. https://doi.org/10.1109/tia.2017.2675363

153.

Banerjee

Sensarma

. Improved analytical method to determine flux-linkage characteristics of a switched reluctance machine. IEEE Trans Ind Appl 2020; 56(6): 6314–6323. https://doi.org/10.1109/tia.2020.3012105

154.

Haque

Chowdhury

, et al. DC-link current ripple reduction in switched reluctance machine drives. IEEE Trans Ind Appl 2021; 57(2): 1429–1439. https://doi.org/10.1109/tia.2021.3053222

155.

Gundogmus

Das

Sozer

, et al. NVH performance improvement in switched reluctance machines by simultaneous radial force and torque ripple minimizations. IEEE Trans Transp Electrification 2024; 10(4): 8019–8029. https://doi.org/10.1109/tte.2024.3355107

156.

Abdalmagid

Bakr

Emadi

, et al. A linesearch-based algorithm for topology and generative optimization of switched reluctance machines. IEEE Trans Transp Electrification 2023; 9(3): 3849–3866. https://doi.org/10.1109/tte.2023.3245992

157.

Ding

Wang

, et al. Finite control set model predictive torque control of switched reluctance motor based on three-phase four-leg inverter. IEEE Trans Ind Electron 2025; 72(10): 9931–9941. https://doi.org/10.1109/tie.2025.3553184

158.

Gan

Chen

, et al. DC-biased sinusoidal current excited switched reluctance motor drives based on flux modulation principle. IEEE Trans Power Electron 2020; 35(10): 10614–10628. https://doi.org/10.1109/tpel.2020.2975121

159.

Fang

Xiao

, et al. Computational-efficient model predictive torque control for switched reluctance machines with linear-model-based equivalent transformations. IEEE Trans Ind Electron 2022; 69(6): 5465–5477. https://doi.org/10.1109/TIE.2021.3091918

160.

Wang

Zhu

Xing

, et al. Multi-indicator synchronous optimization of switched reluctance motor for vehicles considering the multiphysical field coupling. IEEE Trans Transp Electrification 2025; 11(3): 8512–8522. https://doi.org/10.1109/tte.2025.3542132

161.

Sial

Sahoo

. SOGI-FLL-2DOF PID—a combined second-order-generalized-integrator-based FLL and two-degree-of-freedom PID current control for switched reluctance motor drives. IEEE J Emerg Sel Topics Ind Electron 2024; 5(1): 90–103. https://doi.org/10.1109/jestie.2023.3327048

162.

Chaurasiya

Bhattacharya

Das

. Unified four-quadrant control of dual-port solar-assisted switched reluctance drive for electric ferries with power flow management. IEEE Trans Transp Electrification 2024; 10(4): 7941–7953. https://doi.org/10.1109/tte.2024.3443175

163.

Husain

Uddin

Sozer

. Performance comparison of short-pitched and fully pitched switched reluctance machines over wide speed operations. IEEE Trans Ind Appl 2018; 54(5): 4278–4287. https://doi.org/10.1109/tia.2018.2839627

164.

Liu

Chen

Wang

, et al. Wireless switched reluctance motor drive and energy recovery system based on individual demagnetization loop. IEEE Trans Transp Electrification 2025; 11(2): 7016–7028. https://doi.org/10.1109/tte.2024.3522097

165.

Farmahini Farahani

Jalali Kondelaji

Mirsalim

. A new exterior-rotor multiple teeth switched reluctance motor with embedded permanent magnets for torque enhancement. IEEE Trans Magn 2020; 56(2): 1–5. https://doi.org/10.1109/tmag.2019.2955884

166.

Shen

Zhu

Liu

, et al. Development of a laser-guided, embedded-computer-controlled, air-assisted precision sprayer. Trans ASABE 2017; 60(6): 1827–1838. https://doi.org/10.13031/trans.12455

167.

Chen

Ning

, et al. A fuzzy control strategy for the forward speed of a combine harvester based on KDD. Appl Eng Agric 2017; 33(1): 15–22. https://doi.org/10.13031/aea.11299

168.

Zhu

. The generalized predictive control of bacteria concentration in marine lysozyme fermentation process. Food Sci Nutr 2018; 6(8): 2459–2465.

169.

Pang

, et al. Vibration excitation identification and control of the cutter of a combine harvester using triaxial accelerometers and partial coherence sorting. Biosyst Eng 2019; 185: 25–34. https://doi.org/10.1016/j.biosystemseng.2019.02.013

170.

, et al. Adaptive backstepping control of tracked robot running trajectory based on real-time slip parameter estimation. Int J Agric Biol Eng 2020; 13(4): 178–187. https://doi.org/10.25165/j.ijabe.20201304.5739

171.

Niguchi

Hirata

Takahara

, et al. Proposal of a new coil arrangement for a four-phase switched reluctance motor. IEEE Trans Magn 2021; 57(2): 1–6. https://doi.org/10.1109/tmag.2020.3019082

172.

Liu

Yan

Shen

, et al. Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle. Int J Agric Biol Eng 2021; 14(2): 175–181. https://doi.org/10.25165/j.ijabe.20211402.5283

173.

Wang

Wei

. Precision control of spraying quantity based on linear active disturbance rejection control method. Agriculture 2021; 11(8): 761. https://doi.org/10.3390/agriculture11080761

174.

Chen

Zhang

, et al. Research on following suction and discharging motion control method of vacuum-vibration precision seeding manipulator. Appl Eng Agric 2022; 38(6): 873–883. https://doi.org/10.13031/aea.15234

175.

Song

Chen

, et al. Design and experimental testing of a control system for a solid-fertilizer-dissolving device based on fuzzy pid. Agriculture 2022; 12(9): 1382. https://doi.org/10.3390/agriculture12091382

176.

Mohamed

TMK

Gao

Tunio

. Development and experiment of the intelligent control system for rhizosphere temperature of aeroponic lettuce via the Internet of Things. Int J Agric Biol Eng 2022; 15(3): 225–233. https://doi.org/10.25165/j.ijabe.20221503.6530

177.

Shang

, et al. Adaptive sliding mode path tracking control of unmanned rice transplanter. Agriculture 2022; 12(8): 1225. https://doi.org/10.3390/agriculture12081225

178.

, et al. Study on modeling method of a multi-parameter control system for threshing and cleaning devices in the grain combine harvester. Agriculture 2022; 12(9): 1483. https://doi.org/10.3390/agriculture12091483

179.

Xue

Chen

, et al. Robust trajectory tracking control of an autonomous tractor-trailer considering model parameter uncertainties and disturbances. Agriculture 2023; 13(4): 869. https://doi.org/10.3390/agriculture13040869

180.

Mohanraj

Gopalakrishnan

Chokkalingam

, et al. An enhanced model predictive direct torque control of SRM drive based on a novel modified switching strategy for low torque ripple. IEEE J Emerg Sel Top Power Electron 2024; 12(2): 2203–2213. https://doi.org/10.1109/jestpe.2023.3343732

181.

Ronanki

Pittam

Dekka

, et al. Phase current reconstruction method with an improved direct torque control of SRM drive for electric transportation applications. IEEE Trans Ind Appl 2022; 58(6): 7648–7657. https://doi.org/10.1109/tia.2022.3196329

182.

Mohanraj

Umavathi

Verma

, et al. Enhancing performance toward torque and flux control through a hybrid approach of intelligent and DTC for SRM drives. IEEE Access 2025; 13: 92168–92179. https://doi.org/10.1109/access.2025.3569277

183.

Kumar

Singh

. MPC based position sensorless SRM drive for light electric vehicles with optimized voltage vectors and reduced torque ripple. IEEE Trans Ind Appl 2024; 60(2): 3237–3246. https://doi.org/10.1109/tia.2023.3348420

184.

Sun

Diao

Lei

, et al. Direct torque control based on a fast modeling method for a segmented-rotor switched reluctance motor in HEV application. IEEE J Emerg Sel Top Power Electron 2021; 9(1): 232–241. https://doi.org/10.1109/jestpe.2019.2950085

185.

de Paula

Barros

TADS

. A sliding mode DITC cruise control for SRM with steepest descent minimum torque ripple point tracking. IEEE Trans Ind Electron 2022; 69(1): 151–159. https://doi.org/10.1109/tie.2021.3050349

186.

Zhang

Zhai

Zhang

, et al. Staggered-phase spray control: a method for eliminating the inhomogeneity of deposition in low-frequency pulse-width modulation (PWM) variable spray. Agriculture 2024; 14(3): 465. https://doi.org/10.3390/agriculture14030465

187.

Mao

Cai

Guo

, et al. Seed trajectory control and experimental validation of the limited gear-shaped side space of a high-speed cotton precision dibbler. Agriculture 2024; 14(5): 717. https://doi.org/10.3390/agriculture14050717

188.

Sun

Lei

, et al. Torque ripple reduction of SRM drive using improved direct torque control with sliding mode controller and observer. IEEE Trans Ind Electron 2021; 68(10): 9334–9345. https://doi.org/10.1109/tie.2020.3020026

189.

Sun

Wang

Ding

, et al. Adaptive disturbance observer-based fixed time nonsingular terminal sliding mode control for path-tracking of unmanned agricultural tractors. Biosyst Eng 2024; 246: 96–109. https://doi.org/10.1016/j.biosystemseng.2024.06.013

190.

Wang

Yang

Zhang

, et al. Research on the smart broad bean harvesting system and the self-adaptive control method based on CPS technologies. Agronomy 2024; 14(7): 1405. https://doi.org/10.3390/agronomy14071405

191.

Tang

Wang

Zhang

, et al. Linear active disturbance rejection control system for the travel speed of an electric reel sprinkling irrigation machine. Agriculture 2024; 14(9): 1544. https://doi.org/10.3390/agriculture14091544

192.

Sun

Feng

Diao

, et al. An improved direct instantaneous torque control based on adaptive terminal sliding mode for a segmented-rotor SRM. IEEE Trans Ind Electron 2021; 68(11): 10569–10579. https://doi.org/10.1109/tie.2020.3029463

193.

Hao

Guo

, et al. Motor torque distribution strategy for different tillage modes of agricultural electric tractors. Agriculture 2022; 12(9): 1373. https://doi.org/10.3390/agriculture12091373

194.

Ding

Huang

Hang

, et al. Improved efficiency optimization control of SRM based on redefined optimal turn-on angle and its corresponding analytical formula. IEEE Trans Power Electron 2024; 39(12): 16508–16520. https://doi.org/10.1109/tpel.2024.3447038

195.

Song

, et al. Advanced technology of switched reluctance machines in more electric aircraft: a review. IEEE Trans Power Electron 2025; 40(1): 195–216. https://doi.org/10.1109/tpel.2024.3452171

196.

Diao

Sun

Lei

, et al. Multimode optimization of switched reluctance machines in hybrid electric vehicles. IEEE Trans Energy Convers 2021; 36(3): 2217–2226. https://doi.org/10.1109/tec.2020.3046721

197.

Tao

Liu

, et al. A cost-effective phase current sensing scheme involving online error correction for switched reluctance motor drive. IEEE Trans Instrum Meas 2023; 72: 1–11. https://doi.org/10.1109/tim.2023.3324340

198.

Valencia

Tarvirdilu-Asl

Garcia

, et al. Vision, challenges, and future trends of model predictive control in switched reluctance motor drives. IEEE Access 2021; 9: 69926–69937. https://doi.org/10.1109/access.2021.3078366

199.

Liu

Jiang

, et al. Design and control of a transverse flux linear switched reluctance machine for rail transit application. IEEE Access 2022; 10: 43175–43186. https://doi.org/10.1109/access.2022.3169888

200.

Diao

Sun

Lei

, et al. Robust design optimization of switched reluctance motor drive systems based on system-level sequential Taguchi method. IEEE Trans Energy Convers 2021; 36(4): 3199–3207. https://doi.org/10.1109/tec.2021.3085668

201.

Guo

Wang

Chen

, et al. An embedded magnetic encoder-based rotor position estimation for switched reluctance machine. IEEE Trans Transp Electrification 2024; 10(1): 110–121. https://doi.org/10.1109/tte.2023.3253704

202.

Banerjee

Sensarma

. Low-cost realization of feature position estimation scheme for switched reluctance motor. IEEE Trans Power Electron 2023; 38(3): 2850–2854. https://doi.org/10.1109/tpel.2022.3222739

203.

Cao

. Comparative study of permanent magnet assisted linear switched reluctance motor and linear flux switching permanent magnet motor for railway transportation. IEEE Trans Appl Supercond 2020; 30(4): 1–5. https://doi.org/10.1109/tasc.2020.2965874

204.

Sun

Wan

Lei

, et al. Multiobjective and multiphysics design optimization of a switched reluctance motor for electric vehicle applications. IEEE Trans Energy Convers 2021; 36(4): 3294–3304. https://doi.org/10.1109/tec.2021.3078547

205.

Wang

Chen

, et al. Bearing fault diagnosis of switched reluctance motor in electric vehicle powertrain via multisensor data fusion. IEEE Trans Ind Inform 2022; 18(4): 2452–2464. https://doi.org/10.1109/tii.2021.3095086

206.

Kim

Lai

J-S

. Quad sampling incremental inductance measurement through current loop for switched reluctance motor. IEEE Trans Instrum Meas 2020; 69(7): 4251–4257. https://doi.org/10.1109/tim.2019.2949319

207.

Cheng

Liao

Yan

. Development and performance analysis of segmented-double-stator switched reluctance machine. IEEE Trans Ind Electron 2022; 69(2): 1298–1309. https://doi.org/10.1109/TIE.2023.3277073

208.

Sun

Lei

, et al. Torque modeling of a segmented-rotor SRM using maximum-correntropy-criterion-based LSSVR for torque calculation of EVs. IEEE J Emerg Sel Top Power Electron 2021; 9(3): 2674–2684. https://doi.org/10.1109/jestpe.2020.2977957

209.

Guo

Zeng

, et al. A low-speed position sensorless scheme from standstill for low-cost SRM drives based on triple current slope difference threshold. IEEE Trans Ind Electron 2024; 71(4): 3296–3306. https://doi.org/10.1109/TIE.2023.3277073

210.

Gan

Meng

, et al. Online calibration of sensorless position estimation for switched reluctance motors with parametric uncertainties. IEEE Trans Power Electron 2020; 35(11): 12307–12320. https://doi.org/10.1109/tpel.2020.2983103

211.

Fang

Scalcon

Filho

CJV

, et al. A unified wide-speed range sensorless control method for switched reluctance machines based on unsaturated reluctance. IEEE Trans Ind Electron 2023; 70(10): 9903–9913. https://doi.org/10.1109/tie.2022.3224123

212.

Gan

Chen

Sun

, et al. A position sensorless torque control strategy for switched reluctance machines with fewer current sensors. IEEE/ASME Trans Mechatron 2021; 26(2): 1118–1128. https://doi.org/10.1109/tmech.2020.3017864

213.

Xiao

Filho

Fang

, et al. Position-sensorless control of switched reluctance motor drives: a review. IEEE Trans Transp Electrification 2022; 8(1): 1209–1227. https://doi.org/10.1109/tte.2021.3110867

214.

Guo

Zeng

Zhong

, et al. Online self-commissioning of unsaturated inductance characteristics for low-speed position sensorless control in switched reluctance machines. IEEE Trans Ind Electron 2024; 71(6): 5576–5585. https://doi.org/10.1109/tie.2023.3285989

215.

Ding

Wang

, et al. Low-speed sensorless control of switched reluctance motor drives using random pulse injection for noise reduction. IEEE Trans Transp Electrification 2025; 11(1): 5005–5016. https://doi.org/10.1109/tte.2024.3474680

216.

Cai

Yan

Zhang

, et al. A reliable sensorless starting scheme for SRM with lowered pulse injection current influences. IEEE Trans Instrum Meas 2021; 70: 1–9. https://doi.org/10.1109/tim.2020.3040990

217.

Guo

Zeng

, et al. A direct drive sensorless starting scheme based on triple current threshold method in switched reluctance machines for low-cost current sampling applications. IEEE Trans Power Electron 2023; 38(7): 8730–8741. https://doi.org/10.1109/tpel.2023.3265035

218.

Guo

Zeng

Zhong

, et al. High-precision injection current sampling scheme for direct drive low-speed position sensorless control of switched reluctance machine. IEEE Trans Ind Electron 2024; 71(11): 13754–13765. https://doi.org/10.1109/tie.2024.3366219

219.

Xiao

Fang

, et al. Induced current reduction in position-sensorless SRM drives using pulse injection. IEEE Trans Ind Electron 2023; 70(5): 4620–4630. https://doi.org/10.1109/tie.2022.3183275

220.

Guo

Zeng

Zhong

, et al. Automatic offline measurement of full-cycle unsaturated inductance for low-speed rotor position estimation in switched reluctance machines. IEEE J Emerg Sel Top Power Electron 2024; 12(1): 849–861. https://doi.org/10.1109/jestpe.2023.3328967

221.

Sun

Lan

Liu

, et al. Linear inductance model reshaping-based sensorless position estimation method for SRM with antimagnetic saturation capability. IEEE J Emerg Sel Top Power Electron 2023; 11(5): 4799–4807. https://doi.org/10.1109/jestpe.2023.3300881

222.

Cai

Zhang

Peng

, et al. A hybrid sensorless starting strategy for SRM with fault-tolerant capability. IEEE Trans Transp Electrification 2023; 9(2): 2444–2452. https://doi.org/10.1109/tte.2022.3212442

223.

Xiao

Fang

, et al. A regional phase-locked loop-based low-speed position-sensorless control scheme for general-purpose switched reluctance motor drives. IEEE Trans Power Electron 2022; 37(5): 5859–5873. https://doi.org/10.1109/tpel.2021.3130273

224.

Song

Zhong

Yang

, et al. Smooth low-speed sensorless control of switched reluctance machine with heavy load. IEEE Trans Ind Electron 2024; 71(4): 3361–3370. https://doi.org/10.1109/tie.2023.3273278

225.

Gong

Habetler

, et al. Direct position control for ultrahigh-speed switched-reluctance machines based on low-cost nonintrusive reflective sensors. IEEE Trans Ind Appl 2019; 55(1): 480–489. https://doi.org/10.1109/tia.2018.2868037

226.

Wang

Niu

, et al. Estimating rotor position for SRM via regional sliding mode observer. IEEE Trans Ind Electron 2025; 72(1): 230–239. https://doi.org/10.1109/tie.2024.3417973

227.

Ding

Wang

, et al. Sensorless control for switched reluctance motor based on quadrature feature position observer at medium and high speeds. IEEE Trans Ind Electron 2025; 72(9): 8895–8904. https://doi.org/10.1109/tie.2025.3541283

228.

Chen

Jing

Wang

, et al. General-purpose high-speed position-sensorless control of switched reluctance motors using single-phase adaptive observer. IEEE Trans Transp Electrification 2024; 10(3): 5241–5249. https://doi.org/10.1109/tte.2023.3326826

229.

Xiao

Fang

, et al. Improved feature-position-based sensorless control scheme for SRM drives based on nonlinear state observer at medium and high speeds. IEEE Trans Power Electron 2021; 36(5): 5711–5723. https://doi.org/10.1109/tpel.2020.3030007

230.

Chen

Jing

Wang

, et al. High-speed sensorless control strategy of SRMs: utilizing fourth order generalized integrator to enhance control performance and overcome magnetic characteristic constraints. IEEE Trans Ind Appl 2025; 61(1): 335–344. https://doi.org/10.1109/tia.2024.3476243

231.

Zhou

Chen

. Four-quadrant position sensorless operation of switched reluctance machine for electric vehicles over a wide speed range. IEEE Trans Transp Electrification 2021; 7(4): 2835–2847. https://doi.org/10.1109/tte.2021.3070640

232.

Ding

Song

. Position sensorless control of switched reluctance motors using reference and virtual flux linkage with one-phase current sensor in medium and high speed. IEEE Trans Ind Electron 2020; 67(4): 2595–2606. https://doi.org/10.1109/tie.2019.2912764

233.

Chen

Jing

Wang

, et al. Selective harmonic elimination and dynamic enhancement in magnetic-characteristic-free sensorless control of SRM drives at high speeds. IEEE Trans Power Electron 2024; 39(5): 5339–5348. https://doi.org/10.1109/tpel.2024.3364087

234.

Cai

Liu

Zeng

. Aligned position estimation based fault-tolerant sensorless control strategy for SRM drives. IEEE Trans Power Electron 2019; 34(8): 7754–7762. https://doi.org/10.1109/tpel.2018.2877779

235.

Sun

Pan

, et al. Medium-to-high speed range sensorless control of SRM drives based on unaligned rotor position estimation. IEEE Trans Ind Electron 2025; 72: 12185–12194. https://doi.org/10.1109/TIE.2025.3566750

236.

Zhou

Chen

Wang

, et al. Synthetic sensorless control scheme for full-speed range of switched reluctance machine drives with fault-tolerant capability. IEEE Trans Transp Electrification 2022; 8(4): 4456–4469. https://doi.org/10.1109/tte.2022.3192317

237.

Shao

Chai

. Dual closed-loop position observer with nonlinearity-decoupled model for feature-flux-based sensorless SRM drives. IEEE Trans Ind Electron 2024; 71(1): 160–170. https://doi.org/10.1109/TIE.2023.3243289

238.

Sun

Wang

Yao

, et al. Position sensorless control of SRMs based on improved sliding mode speed controller and position observer. IEEE Trans Ind Electron 2025; 72(1): 100–110. https://doi.org/10.1109/tie.2024.3398685

239.

Xiao

Fang

, et al. Magnetic-characteristic-free high-speed position-sensorless control of switched reluctance motor drives with quadrature ﬂux estimators. IEEE J Emerg Sel Top Power Electron 2022; 10(1): 220–235. https://doi.org/10.1109/JESTPE.2021.3088064

240.

Bonah

Huang

, et al. Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm. J Food Process Eng 2019; 42(6): e13236. https://doi.org/10.1111/jfpe.13236

241.

Tang

Sun

Yao

, et al. Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling-whale optimization algorithm-support vector machine. J Food Process Eng 2021; 44(1): e13603. https://doi.org/10.1111/jfpe.13603

242.

Tian

Sun

Zhou

, et al. Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression–support vector machine algorithm and visible-near infrared hyperspectral imaging. J Food Process Eng 2020; 43(8): e13432. https://doi.org/10.1111/jfpe.13432

243.

Peng

Zhao

Liu

. Fused deep features-based grape varieties identification using support vector machine. Agriculture 2021; 11(9): 869. https://doi.org/10.3390/agriculture11090869

244.

Zhu

Wang

Pan

, et al. Task allocation of multi-machine collaborative operation for agricultural machinery based on the improved fireworks algorithm. Agronomy 2024; 14(4): 710. https://doi.org/10.3390/agronomy14040710

245.

Wang

Hong

Fang

, et al. Optimized design for vibration reduction in a residual film recovery machine frame based on modal analysis. Agriculture 2024; 14(4): 543. https://doi.org/10.3390/agriculture14040543

246.

Zhou

Sun

Tian

, et al. Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce. Food Chem 2020; 321: 126503. https://doi.org/10.1016/j.foodchem.2020.126503

247.

Zhou

, et al. Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm. J Food Process Preserv 2020; 44(8): e14561. https://doi.org/10.1111/jfpp.14561

248.

Jiang

Liu

, et al. Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: comparing the effects of swarm intelligence optimization algorithms on sensor features. Food Chem 2021; 338: 127828. https://doi.org/10.1016/j.foodchem.2020.127828

249.

Lian

Chen

Guan

, et al. Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm. Int J Agric Biol Eng 2021; 14(1): 224–229. https://doi.org/10.25165/j.ijabe.20211401.5731

250.

Pang

Tang

, et al. Synchronization optimization of pipe diameter and operation frequency in a pressurized irrigation network based on the genetic algorithm. Agriculture 2022; 12(5): 673. https://doi.org/10.3390/agriculture12050673

251.

Cai

Wang

, et al. Research on rotor position model for switched reluctance motor using neural network. IEEE/ASME Trans Mechatron 2018; 23(6): 2762–2773. https://doi.org/10.1109/TMECH.2018.2870892

252.

Yalavarthi

Singh

. Sensorless speed control of SRM drive using optimized neural network model for rotor position estimation. IEEE Trans Energy Convers 2024; 39(4): 2158–2168. https://doi.org/10.1109/tec.2024.3401483

253.

Huang

Kou

Zhao

. Multi-objective optimization of the interior permanent magnet synchronous motor for electric vehicles. In: Proceedings 2023 IEEE 6th international electrical and energy conference (CIEEC), Hefei, China, May 2023, pp.833–836.

254.

Shen

Zhang

Chen

, et al. A static current error elimination algorithm for predictive current control in PMSM for electric vehicles. Green Energy Intell. Transp 2026; 5(1): 100306. https://doi.org/10.1016/j.geits.2025.100306

255.

Tang

Sun

Yao

. An overview of position sensorless techniques for switched reluctance machine systems. Appl Sci 2022; 12(7): 3616. https://doi.org/10.3390/app12073616