Title :
A fuzzy-neural adaptive control for MIMO nonlinear system with application
Author :
Yingguo, Piao ; Zhenqiang, Yang ; Yanquan, Li ; Ming, Li ; Xiqin, He
Author_Institution :
Inst. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
The fuzzy identification proposed by Takaki and Sugeno (1985) is extended to a MIMO adaptive controller based on a hybrid neural network structure. The network is roughly divided into the premise and consequence corresponding to the T-S model. Each parameter of the consequence function can be adjusted by the extended Bp algorithm so that automatic rule modification can be realized. The membership function of each fuzzy subset can be modified by a genetic algorithm. In this way, more pre-knowledge for the plant need not be required. Finally, the MIMO fuzzy-neural control is used to simulate a real example
Keywords :
MIMO systems; adaptive control; boilers; fuzzy control; fuzzy set theory; genetic algorithms; identification; neurocontrollers; nonlinear control systems; MIMO nonlinear system; T-S model; automatic rule modification; extended Bp algorithm; fuzzy identification; fuzzy subset; fuzzy-neural adaptive control; hybrid neural network structure; membership function; Adaptive control; Artificial neural networks; Automatic control; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy set theory; MIMO; Neural networks; Programmable control;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863370
