A Random Spatial lbest PSO-Based Hybrid Strategy for Designing Adaptive Fuzzy Controllers for a Class of Nonlinear Systems

被引:15
作者
Das Sharma, Kaushik [1 ]
Chatterjee, Amitava [2 ]
Rakshit, Anjan [2 ]
机构
[1] W Bengal Univ Technol, Kalyani Govt Engn Coll, Dept Elect Engn, Kalyani 741235, W Bengal, India
[2] Jadavpur Univ, Dept Elect Engn, Kolkata 700032, India
关键词
Global particle swarm optimization based approach (gPSOBA); hybrid fuzzy controller; lbest particle swarm optimization (PSO); Lyapunov theory; self-adaptive fuzzy controller;
D O I
10.1109/TIM.2012.2187359
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
In this paper, a new variant of particle swarm optimization (PSO), called random spatial lbest PSO model, is proposed and implemented for designing a newly devised stable adaptive hybrid fuzzy controller. The newly developed concurrent hybrid strategy for designing fuzzy controllers utilizes the conventional Lyapunov theory and the proposed PSO-based stochastic approach. The objective is to design a self-adaptive fuzzy controller online, optimizing both its structures and free parameters such that the designed controller can guarantee the desired stability and simultaneously provide satisfactory transients performance. The global version and two different lbest variants of PSO schemes and the proposed random spatial lbest model of PSO are employed for three popular, challenging, and nonlinear processes, and the proposed controller emerges as the superior algorithm in terms of tracking performance overall. These results aptly demonstrate the usefulness of the proposed approach.
引用
收藏
页码:1605 / 1612
页数:8
相关论文
共 20 条
[1]
[Anonymous], 2002, Computational Intelligence an Introduction
[2]
Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm [J].
Chiang, CL ;
Su, CT .
ELECTRIC POWER SYSTEMS RESEARCH, 2005, 73 (02) :239-247
[3]
DasSharma Kaushik, 2008, SICE 2008 - 47th Annual Conference of the Society of Instrument and Control Engineers of Japan, P1839, DOI 10.1109/SICE.2008.4654961
[4]
A FUZZY CONTROLLER FOR VEHICLE RENDEZVOUS AND DOCKING [J].
EATHERLEY, GJ ;
PETRIU, EM .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 1995, 44 (03) :810-814
[5]
Eberhart RC, 2000, IEEE C EVOL COMPUTAT, P84, DOI 10.1109/CEC.2000.870279
[6]
Esmin A.A. A., 2002, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, I, P108
[7]
An improved stable adaptive fuzzy control method [J].
Fischle, K ;
Schröder, D .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1999, 7 (01) :27-40
[8]
Combining genetic algorithms and lyapunov-based adaptation for online design of fuzzy controllers [J].
Giordano, Vincenzo ;
Naso, David ;
Turchiano, Biagio .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2006, 36 (05) :1118-1127
[9]
Frequency Calibration Based on the Adaptive Neural-Fuzzy Inference System [J].
Hsu, Wang-Hsin ;
Tu, Kun-Yuan ;
Wu, Jung-Shyr ;
Liao, Chia-Shu .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2009, 58 (04) :1229-1233
[10]
Multiple fuzzy reference model adaptive controller design for pitch-rate tracking [J].
Kamalasadan, Sukumar ;
Ghandakly, Adel A. .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2007, 56 (05) :1797-1808