Robust intelligent tracking control with PID-type learning algorithm

Authors:
Chun-Fei Hsu;Guan-Ming Chen;Tsu-Tian Lee
Affiliations:
Department of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan, ROC;Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan, ROC;Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan, ROC
Venue:
Neurocomputing
Year:
2007

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Abstract

This paper proposes a robust intelligent tracking controller (RITC) for a class of unknown nonlinear systems. The proposed RITC system is comprised of a neural controller and a robust controller. The neural controller is designed to approximate an ideal controller using a proportional-integral-derivative (PID)-type learning algorithm in the sense of Lyapunov function, and the robust controller is designed to achieve L^2 tracking performance with desired attenuation level. Finally, to investigate the effectiveness of the RITC system, the proposed design methodology is applied to control two chaotic dynamical systems. The simulation results verify that the proposed RITC system using PID-type learning algorithm can achieve faster convergence of the tracking error and controller parameters than that using I-type learning algorithm.