Study of Strip Flatness and Gauge Complex Control Based on Improved PSO-RBF Neural Networks

Author

Xu, Lin ; Fang, Xiaoke ; Fang, Qichao ; Wang, Jianhui ; Gu, Shusheng

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ., ShenYang

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6397

Lastpage

6400

Abstract

An improved particle swarm optimization (IPSO) is presented to solve the premature and low precision based on shrinking chaotic mutation with population´s fitness, which is used to train the radius basis function (RBF) neural networks, and optimization parameters of the network. Considering the stronger nonlinearity and coupling of strip flatness and gauge complex control, using the IPSO-RBF neural networks as a controller, an AFC-AGC system is designed. The simulation results show that the design method is simple and effective and has good performance of adaptively tracking target and resistance to disturbances, and provides a new way for strip flatness and gauge complex control

Keywords

gauges; large-scale systems; neurocontrollers; nonlinear control systems; particle swarm optimisation; radial basis function networks; strips; target tracking; RBF neural network; gauge complex control; network parameter optimization; particle swarm optimization; radius basis function; shrinking chaotic mutation; strip flatness; Chaos; Control systems; Couplings; Design methodology; Genetic mutations; Neural networks; Nonlinear control systems; Particle swarm optimization; Strips; Target tracking; RBF neural networks; flatness and gauge complex control; particle swarm optimization; shrinking chaotic mutation;

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.2006.1714316

Filename

1714316