Abstract
Advanced control and optimization has played a key role in modern engineering systems. The past decade has witnessed the substantial development in integrating advanced intelligent computing techniques with modern control engineering, leading to the proposal of a number of new and novel methods and technologies in coping with complex engineering problems. This special issue contains eight papers selected from the 2012 International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE’12) held on 12–13 September 2012, in Shanghai, China. They cover advanced control and optimization applications in a range of complex engineering systems, in particular in energy and power systems penetrated by significant renewable generations and large adoption of electric vehicles. The papers included in this special issue are summarized below.
High wind power penetration presents a number of significant challenges to reliable and flexible power system operation, and proactive demand response plays a significant role to meet these challenges. Yet, how the demand response affects the equilibrium of electricity market has not been fully addressed. An, Zhang and Li proposed a Cournot equilibrium model for electricity markets with interruptible load program and wind power, which was then solved by introducing a novel case-by-case computational approach, examining the different relations between the market price and trigger price.
As a key measure to decarbonizing the transport sector, electric vehicles (EVs) have received much attentions in recent years. However, the introduction of EVs poses major challenges for power system operation. Liu and McLoone investigated the effectiveness of a distributed charging scheme algorithm at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. Their simulation results show that that by using the proposed smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.
The dynamic voltage restorer (DVR) is a new type of dynamic power quality compensator in modern power systems and smart grid applications. It can detect the supply voltage in real time and generate a dynamic compensation voltage to compensate for the dropped voltage. To compensate for both low-order harmonics and voltage sag, Wang et al. proposed a novel double closed-loop digital control strategy by introducing a fundamental proportional resonant (PR) control in the voltage loop and while the selective harmonic PR control in an inductance current loop.
Due to many distinctive advantages such high power density, high efficiency, high reliability and fast dynamics, permanent-magnet synchronous motors (PMSMs) have been widely used, including in electric vehicles. Yet, their control performance is significantly affected by the changes in the mechanical parameters, external loads, disturbances and perturbations. To attain high control performance, Qi, Bao and Shi introduced a novel second-order integral sliding mode control (SOSMC) algorithm for the velocity control of PMSMs, where an integral manifold is utilized to reduce the static error during the sliding mode movement phase and the new SOSMC law is derived by a Lyapunov function approach to guarantee the system convergence.
Flexible manipulators are easy to transfer an input force to another point through elastic deformation and have found many applications in the manufacturing industry. Yet, it is a highly coupled non-linear coupled system, which is difficult to model and control. Cheng proposed an adaptive neural-fuzzy control scheme for a dual-level-structure flexible manipulator with variable dynamic payload, where a neural-fuzzy controller in the feedback channel and an image-guided identification network (IGIN) are combined to achieve good tracking performance.
Many industrial control applications involve constraints on the system states and inputs, and model predictive control (MPC) has widely recognized as an effective control methodology for handling such cases. Li et al. proposed an improved model predictive control scheme for polytopic systems subject to exogenous disturbance and performance constraints by introducing additional free parameters and using a dilated linear matrix inequality technique.
Irrigation canals and rivers have played a key role in human civilization, and the dynamics of water flow in open channels are well described by the famous Saint-Venant equations. Yet, its precise control presents one of the biggest challenges. Cen et al. proposed a boundary feedback control design for open canal networks using the linearization of boundary conditions. For open canal networks with any type of cross-section, which can be modelled by the Saint-Venant equations, the characteristic form in terms of Riemann invariants was first established, based on which a stabilizing boundary control law was developed by linearizing the boundary conditions for both a single reach and the open-channel network composed by multi-reaches in a cascade.
Inventory management has long been a focus in the supply chain management and it is shown that the inventory system may account for 20–60% of the total value in the manufacturing industry. Yet, this is an extremely complicated optimization problem, and Lin and Song considered a three-layer supply chain consisting of suppliers, manufacturers and retailers, and a robust multi-objective model was built and then solved by a hybrid Non-Dominated Genetic Algorithm-II (NSGA-II), where a polynomial time algorithm was designed. Furthermore, a local search method was tailored for the NSGA-II to improve solutions.
These eight papers only serve as an introduction to the recent advances in intelligent control and optimization in solving complex engineering problems. We would like to express our deepest gratitude to the reviewers who have helped with the review process for this special issue, and to the authors for contributing their papers.