Actuators with rate and amplitude saturations and time delay are natural parts of autonomous systems. Accurate identification of these actuators is a prominent issue in controller design process. Nonlinear behavior and time delay characteristics have caused more complexity in identification. Various references have dealt with control of autonomous systems considering dynamics of actuators; however, the identification topic has not been explored properly. This article illustrates how meta-heuristic optimization techniques can be used to precisely identify such systems. At first, actuator model as a nonlinear system is considered as a combination of saturation elements, linear transfer function and an inserted time delay. Afterward, actuator model is identified using several optimization methods. The proposed scheme is simulated on a hypothetical system and its performance is demonstrated. Then, it is employed in an identification problem of a realistic actuator. The results revealed that the proposed method has appropriate performance in identification of actuators’ dynamics.
BernstenDSMichelAN. A chronological bibliography on saturating actuators. Int J Robust Nonlin1995; 5: 375–380.
2.
JacksonPA. Overview of missile flight control systems. J Hopkins APL Tech D2010; 29: 9–24.
3.
KapilaVGrigoriadisKM. Actuator saturation control. New York: Marcel Dekker, 2002.
4.
DornheimMA. Report pinpoints factors leading to YF-22 crash. Aviat Week Space Tec1992; 137(19): 53–54.
5.
ShifrinCA. Sweden seeks cause of Gripen crash. Aviat Week Space Tec1993; 139(7): 78–79.
6.
BriegerOKerrMLeißlingD. Flight testing of a rate saturation compensation scheme on the ATTAS aircraft. Aerosp Sci Technol2009; 13: 92–104.
7.
DudaH. Flight control system design considering rate saturation. Aerosp Sci Technol1998; 4: 265–275.
8.
ParkS. Deflection limit in open-loop onset-point pilot-in-the-loop oscillation analysis. J Guid Control Dynam2014; 27(2): 692–695.
9.
TzengCLinK. Adaptive ship steering autopilot design with saturating and slew rate limiting actuator. Int J Adapt Control2000; 14: 411–426.
10.
KahveciNEIoannouPA. Parameter identification and adaptive control for improved vehicle yaw stability performance. In: 49th IEEE conference on decision and control, Hilton Atlanta Hotel, Atlanta, GA, 15–17 December 2010, pp.2904–2909. New York: IEEE.
11.
GaleaniSOnoriSTeelAR. A magnitude and rate saturation model and its use in the solution of a static anti-windup problem. Syst Control Lett2008; 57: 1–9.
12.
LeonessaAHaddadWMHayakawaT. Adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints. Int J Adapt Control2009; 23(1): 73–96.
13.
KefferpützKFischerBAdamyJ. A nonlinear controller for input amplitude and rate constrained linear systems. IEEE T Automat Contr2013; 58(10): 2693–2697.
14.
SarhadiPGhahramaniNShafeenejhadI. Identification of a non-linear hydraulic actuator considering rate saturation using particle swarm optimization algorithm. Int J Model Ident Contr2013; 18(2): 136–145.
15.
GaoHChenTLamJ. A new delay system approach to network-based control. Automatica2008; 44: 39–52.
16.
ChenFGarnierHGilsonM. Robust identification of continuous-time models with arbitrary time-delay from irregularly sampled data. J Process Contr2015; 25: 19–27.
17.
YinSLiXGaoH. Data-based techniques focused on modern industry: an overview. IEEE T Ind Electron2015; 62(1): 657–667.
18.
LiuC. Sensitivity analysis and parameter identification for a nonlinear time-delay system in microbial fed-batch process. Appl Math Model2014; 38: 1449–1463.
19.
SalloumRMoaveniBArvanMR. Identification and robust controller design for an electromechanical actuator with time delay. T I Meas Control. Epub ahead of print 10November2014. DOI: 10.1177/0142331214556799.
20.
QianYOuGMagharehA. Parametric identification of a servo-hydraulic actuator for real-time hybrid simulation. Mech Syst Signal Pr2014; 48: 260–273.
21.
BoussaïdILepagnotJSiarryP. A survey on optimization metaheuristics. Inform Sciences2013; 237: 82–117.
22.
KristinssonKDumontGA. System identification and control using genetic algorithms. IEEE T Syst Man Cyb1992; 22(5): 1033–1046.
23.
SugieTWadaT. Versatility of particle swarm optimization in identification for continuous-time systems. In: ICROS-SICE international joint conference, Fukuoka International Congress Center, Fukuoka, Japan, 18–21 August 2009, pp.4908–4912. New York: IEEE.
24.
LinWMSuTJWuRC. Parameter identification of induction machine with a starting no-load low-voltage test. IEEE T Ind Electron2012; 59(1): 352–360.
25.
LiQChenWWangY. Parameter identification for PEM fuel-cell mechanism model based on effective informed adaptive particle swarm optimization. IEEE T Ind Electron2011; 58(6): 2410–2419.
26.
MohideenKASaravanakumarGValarmathiK. Real-coded Genetic Algorithm for system identification and tuning of a modified Model Reference Adaptive Controller for a hybrid tank system. Appl Math Model2013; 37: 3829–3847.
27.
DuYFangJMiaoC. Frequency-domain system identification of an unmanned helicopter based on an adaptive genetic algorithm. IEEE T Ind Electron2014; 16(2): 870–881.
28.
GaoFFeiF-XXuQ. A novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems. Appl Math Comput2012; 219: 552–568.
29.
LariAKhosraviARajabiF. Controller design based on μ analysis and PSO algorithm. ISA T2014; 53: 517–523.
30.
MarutaIKimTHSugieT. Fixed-structure H-infinity controller synthesis: a meta-heuristic approach using simple constrained particle swarm optimization. Automatica2009; 45(2): 553–559.
31.
CoelhoLSGuerraFABatistelaNJ. Multiobjective cuckoo search algorithm based on Duffing’s oscillator applied to Jiles-Atherton vector hysteresis parameters estimation. IEEE T Magn2013; 49(5): 1745–1748.
32.
AhmadiARTavakolpour-SalehARYazdaniAM. Imperialist competitive algorithm-based adaptive fuzzy control of a pneumatic actuator. Proc IMechE, Part I: J Systems and Control Engineering2014; 228(1): 26–41.
33.
SteinG. Respect the unstable. IEEE Contr Syst Mag2003; 23(4): 12–25.
34.
O’BrienJF. Rudder roll stabilization by nonlinear dynamic compensation. US patent 8567331 B2, 2013.
35.
GoldbergDE. Genetic algorithms in search optimizations and machine learning. Boston, MA: Addison-Wesley, 1989.
36.
EngelbrechtAP. Computational intelligence: an introduction. 2nd ed.Hoboken, NJ: John Wiley & Sons, 2007.
37.
KennedyJEberhartRC. Particle swarm optimization. In: Proceedings of the IEEE international joint conference on neural networks, Perth, WA, Australia, 27 November–1 December 1995. New York: IEEE.
38.
KarabogaD. An idea based on honey bee swarm for numerical optimization. Technical report TR06, October2005. Kayseri, Turkiye: Erciyes University.
39.
KarabogaDAkayB. Proportional–integral–derivative controller design by using artificial bee colony, harmony search, and the bees algorithms. Proc IMechE, Part I: J Systems and Control Engineering2010; 224: 869–883.
40.
GorginpourHOraeeHMcMahonRA. Electromagnetic-thermal design optimization of the brushless doubly fed induction generator. IEEE T Ind Electron2014; 61(4): 1710–1721.
