Adaptive recurrent fuzzy neural network control for linearized multivariable systems

Author

Lin, Chih-Min ; Chen, Chiu-Hsiung ; Chin, Wei-Laing

Author_Institution

Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li, Taiwan

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

709

Abstract

This paper develops a design method of recurrent fuzzy neural network (RFNN) control system for multi-input multi-output (MIMO) dynamic systems. This control system consist a feedback controller and a RFNN controller. The feedback controller reveals basic stabilizing controller to stabilize the system and the RFNN controller presents a robust controller to deal with unknown part of system dynamics. The adaptive laws of RFNN are derived based on the Lyapunov stability function so that the stability of the system can be guaranteed. Finally, the proposed control system is applied to an F-16 flight control system. Simulation results demonstrate that the developed control system can achieve favorable robust control performances even with some failures of the flight control system.

Keywords

Lyapunov methods; MIMO systems; adaptive control; aerospace control; control system synthesis; feedback; fuzzy control; fuzzy neural nets; linear systems; multivariable control systems; neurocontrollers; recurrent neural nets; robust control; F-16 flight control system; Lyapunov stability function; MIMO dynamic systems; adaptive control; adaptive laws; control system design; feedback controller; linearized multivariable systems; multiple input multiple output dynamic systems; recurrent fuzzy neural network controller; robust controller; Adaptive control; Aerospace control; Control systems; Design methodology; Fuzzy control; Fuzzy neural networks; Lyapunov method; MIMO; Programmable control; Robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on

ISSN

1098-7584

Print_ISBN

0-7803-8353-2

Type

conf

DOI

10.1109/FUZZY.2004.1375485

Filename

1375485