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		Yue FU;Tianyou CHAI.[en_title][J].Control Theory and Technology,2007,5(2):121~126.[Copy]

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YueFU;TianyouCHAI
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Received:February 24, 2006Revised:November 29, 2006

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Multiple-model-and-neural-network-based nonlinear multivariable adaptive control

Yue FU;Tianyou CHAI

(Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang Liaoning 110004, China;Research Center of Automation, Northeastern University, Shenyang Liaoning 110004, China)

Abstract:

A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

Key words: Adaptive control Neural network Multiple models Switching