Robust adaptive fuzzy tracking control for a class of MIMO systems: a minimal-learning-parameters algorithm

Authors:
Tieshan Li;Gang Feng;Zaojian Zou;Yanjun Liu
Affiliations:
Navigation College, Dalian Maritime University, Dalian, China and School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of MEEM, City University of Hong Kong, Hong Kong;School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Mathematics and Physics, Liaoning University of Technology, Jinzhou, China
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

A robust adaptive fuzzy tracking control problem is discussed for a class of uncertain MIMO nonlinear systems with strongly coupled interconnections. T-S fuzzy systems are used to approximate the unknown system uncertainties. Combining "dynamic surface control(DSC)" approach with "minimal learning parameters(MLP)" algorithm, a systematic procedure for controller design is developed. The key features of the proposed scheme are that, firstly, the problem of "explosion of complexity" inherent in the conventional backstepping method is circumvented, secondly, the number of parameters updated on line for each subsystem is reduced dramatically to 2, one for T-S fuzzy system and the other for the bound of disturbances, and, thirdly, the possible controller singularity problem in some of the existing adaptive control schemes with feedback linearization techniques is removed. These features result in a much simpler algorithm, which is easy to be implemented in application. It is shown that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB) based on Lyapunov theory. Finally, simulation results via a numerical example validate the effectiveness and performance of the proposed scheme.