Model reference adaptive control for multi-input multi-output nonlinear systems using neural networks

Author

Phuah, Jiunshian ; Lu, Jianming ; Yahagi, Takashi

Author_Institution

Graduate Sch. of Sci. & Tech., Chiba Univ., Japan

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

12

Abstract

This paper presents a method of MRAC (model reference adaptive control) for multi-input multi-output (MIMO) nonlinear systems using NNs (neural networks). The control input is given by the sum of the output of the NN (neural network). The NN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual MRAC. The role of the NN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.

Keywords

MIMO systems; model reference adaptive control systems; neurocontrollers; nonlinear control systems; MIMO; MRAC; linearized model; model reference adaptive control; multiinput multioutput nonlinear systems; neural networks; output error minimization; plant dynamics; Adaptive control; Control nonlinearities; Ear; MIMO; Neural networks; Nonlinear control systems; Nonlinear systems; Polynomials; Programmable control; Regulators;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Intelligent Mechatronics, 2003. AIM 2003. Proceedings. 2003 IEEE/ASME International Conference on

Print_ISBN

0-7803-7759-1

Type

conf

DOI

10.1109/AIM.2003.1225064

Filename

1225064