Adaptive fuzzy tracking control of nonlinear systems

  • Authors:

  • Song-Shyong Chen;Yuan-Chang Chang;Chen Chia Chuang;Chau-Chung Song;Shun-Feng Su

  • Affiliations:

  • Department of Information Networking Technology, Hsiu-Ping Institute of Technology, Taiwan, R.O.C.;Department of Electrical Engineering, Lee-Ming Institute of Technology, Taiwan, R.O.C.;Department of Electrical Engineering, National I-Lan University, Taiwan, R.O.C.;Department of Aeronautical Engineering, National Formosa University, Taiwan, R.O.C.;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, R.O.C.

  • Venue:
  • WSEAS Transactions on Systems and Control
  • Year:
  • 2007

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Abstract

Adaptive linearization controllers have been shown to have nice control performance. However, two functions in the controllers are derived from the considered system. Thus, those controllers can only work for known systems. In this paper, we proposed a fuzzy modeling approach to model those two functions. The proposed approach is called the adaptive model reference fuzzy control. In this approach, the considered dynamic nonlinear model can be unknown. Different from previous adaptive fuzzy controllers, our approach does not need any auxiliary operations on input trajectories and on system states. The proposed controller and the weight update laws only need system states and the current desired output without using any their derivatives. The Lyapunov stability theorem is used to derive controller parameters update laws, which ensure that the system states be bounded and the plant output asymptotically tracks an arbitrary piecewise reference trajectory. The proposed method is successfully applied to an unstable nonlinear system and a chaotic system. The learning and control performance of our approach is nice and also superior to that of previous approaches.