Adaptive inverse control based on particle swarm optimization algorithm

Author

Wang, YuShen ; Wang, Kejun ; Qu, JiaSheng ; Yang, YuRong

Author_Institution

Coll. of Autom., Harbin Eng. Univ., China

Volume

4

fYear

2005

fDate

29 July-1 Aug. 2005

Firstpage

2169

Abstract

Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure in training the network of control system was given by using particle swarm. Double inverted pendulum system was used for research object in simulation. The result of experiment proved that this algorithm can obtain more stability performance, and easy to achieve.

Keywords

learning (artificial intelligence); model reference adaptive control systems; neurocontrollers; nonlinear control systems; particle swarm optimisation; pendulums; stability; inverted pendulum system; model reference adaptive inverse control system; neural networks; object inverse model; particle swarm optimization algorithm; stability performance; Adaptive control; Adaptive systems; Automatic control; Control system synthesis; Control systems; Inverse problems; Neural networks; Particle swarm optimization; Programmable control; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2005 IEEE International Conference

Print_ISBN

0-7803-9044-X

Type

conf

DOI

10.1109/ICMA.2005.1626900

Filename

1626900