Stable multiple model adaptive control of nonlinear multivariable discrete-time systems

Authors:
Yue Fu;Tianyou Chai;Hong Wang
Affiliations:
Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, Liaoning Province, China and Research Center of Automation, Northeastern Univ ...;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, Liaoning Province, China and Research Center of Automation, Northeastern Univ ...;Control Systems Center, School of Electrical and Electronic Engineering, The University of Manchester, Manchester, The United Kingdom
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

In this paper, to further relax the restriction on the higher order nonlinearity in [7], a stable multiple model adaptive control (SMMAC) method is developed. First a new robust adaptive controller is designed, which can guarantee the stability of the closed-loop system. Then to improve the system performance, the SMMAC method is presented by switching between the robust adaptive controller and a conventional neural network (NN) adaptive controller. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.