PID Controller Based Adaptive GA and Neural Networks

Author

Sun, Lei ; Mei, Tao ; Yao, Yansheng ; Cai, Linqin ; Meng, Max Q H

Author_Institution

Center for Biomimetic Sensing & Control Res. Inst. of Intelligent Machines, Chinese Acad. of Sci., Hefei

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6564

Lastpage

6568

Abstract

A self-tuning PID controller based on adaptive genetic algorithm (AGA) and neural networks is presented. AGA optimizes not only the initial weights of the BP neural networks (BPNN) which optimizes parameters of PID, but also the optimum values of the following radial basis function neural networks (RBFNN) parameters: centers, variance and weights of the output layer. RBFNN identifies the Jacobian information of the controlled plant. The influence on the control performance is solved which results from the initial parameters of BPNN and RBFNN. The result of the simulation shows that the method can improve the robust performance of the control system

Keywords

genetic algorithms; neurocontrollers; radial basis function networks; robust control; self-adjusting systems; three-term control; BP neural networks; Jacobian information; PID parameters optimization; adaptive genetic algorithm; control system; radial basis function neural networks; robust performance; self-tuning PID controller; Adaptive control; Automatic control; Automation; Biomimetics; Control system synthesis; Iterative algorithms; Machine intelligence; Neural networks; Programmable control; Three-term control; PID; RBF; adaptive GA; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714351

Filename

1714351