DocumentCode :
2602692
Title :
An observer based adaptive PID controller
Author :
Yao, Leehter ; Wen, Hong-Kang
Author_Institution :
Dept. of Electr. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan
fYear :
2011
fDate :
26-29 June 2011
Firstpage :
511
Lastpage :
516
Abstract :
An Adaptive Fuzzy PID Controller with Genetic Algorithm (GA) to tune its parameters is proposed in this paper. The task of the controller is to track the trajectory of a nonlinear system as best as it could. The Lyapunov´s direct method is used as a tool for nonlinear system analysis and design. In which, the Lyapunov´s linearization method is proven here to be useful for linear control. The paper relies on linearization method and the direct method to formulate its stability theory. The controller has two states, a learning state and a controlling state where GA performs on-line tuning of the controller´s parameters. The GA method has the effect of tuning PID parameters to meet operation time constraint and system performance. In the controlling state, there are a supervisory controller designed to ensure system stability and a compensator appended to compensate for modeling error and disturbance. Overall performance of the controller is not compromised by the fact that it has only three parameters to work with.
Keywords :
Lyapunov methods; adaptive control; control system synthesis; genetic algorithms; linear systems; nonlinear control systems; observers; three-term control; GA; Lyapunov direct method; Lyapunov linearization method; PID parameters; adaptive fuzzy PID controller; controlling state; genetic algorithm; learning state; linear control; nonlinear system; nonlinear system analysis; nonlinear system design; observer; stability theory; supervisory controller design; Adaptive systems; Biological cells; Genetic algorithms; Nonlinear systems; Observers; Trajectory; Tuning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on
Conference_Location :
Shanghai
Type :
conf
DOI :
10.1109/ICMIC.2011.5973758
Filename :
5973758
Link To Document :
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