Online Decoupling and Control of Multivariable Nonlinear System Based on Neural Network

Author

Li, X.L. ; Yang, L. ; Bai, Y.

Author_Institution

North China Electr. Power Univ., Baoding

fYear

2006

fDate

24-26 May 2006

Firstpage

1

Lastpage

5

Abstract

Aimed at neural network can approach any nonlinear dynamic system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. The learning algorithm of NN is online, which makes a set of cross-correlation function as the target function, and adopts hybrid genetic algorithm to train neural decoupler. When the decoupling is completed, the single nerve cell self-adaptor PID is used to control two SISO systems. The efficiency of the algorithm has been shown by numerical simulations combining nonlinear system

Keywords

control engineering computing; genetic algorithms; multivariable control systems; neurocontrollers; nonlinear control systems; numerical analysis; three-term control; SISO systems; cross-correlation function; distributed neural network decoupling system; hybrid genetic algorithm; multivariable nonlinear system control; neural decoupler; neural network; nonlinear dynamic system; numerical simulations; online decoupling; self-adaptor PID; target function; Automatic control; Control systems; Distributed control; Genetic algorithms; MIMO; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Vehicle dynamics; Decoupling; Genetic algorithm; Neural network; Nonlinear system; Online learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications, 2006 1ST IEEE Conference on

Conference_Location

Singapore

Print_ISBN

0-7803-9513-1

Electronic_ISBN

0-7803-9514-X

Type

conf

DOI

10.1109/ICIEA.2006.257152

Filename

4025770