Journal of Applied Science and Engineering

Published by Tamkang University Press

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Yau-Zen Chang This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Zhi-Ren Tsai2

1Department of Mechanical Engineering, Chang Gung University, Tao-Yuan, Taiwan 333, R.O.C.
2Department of Electrical Engineering, Chang Gung University, Tao-Yuan, Taiwan 333, R.O.C.


 

Received: April 26, 2006
Accepted: August 15, 2006
Publication Date: September 1, 2007

Download Citation: ||https://doi.org/10.6180/jase.2007.10.3.03  


ABSTRACT


The progress of parallel distributed control (PDC) scheme has successfully exploited the achievements of linear control theories to the fuzzy control of nonlinear systems. However, the design of control gain for each local systems dynamics is restricted by a common Lyapunov function. The lately proposed idea of fuzzy Lyapunov function is a promising analysis tool to relieve the restriction. The purpose of this paper is to remove some unnecessary constrains and complexities of the original contribution, and to extend its usability by considering model uncertainty in the closed-loop control design. In completing the design problem, all the conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved iteratively by any efficient optimization methods, such as genetic algorithms. A design and analysis example of the Lorenz system is given to illustrate the effectiveness of the proposed approach.


Keywords: Fuzzy System Identification, Fuzzy Lyapunov Function, Linear Matrix Inequality (LMI), Uncertain Chaotic System


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