Compact ant colony optimization algorithm based fuzzy neural network backstepping controller for MIMO nonlinear systems

Author

Chen, Chao-Kuang ; Leu, Yih-Guang ; Wang, Wei-Yen ; Chen, Chun-Yao

Author_Institution

Dept. of Ind. Educ., Nat. Taiwan Normal Univ., Taipei, Taiwan

fYear

2010

Firstpage

146

Lastpage

149

Abstract

In this paper, a compact ant colony algorithm used to tune parameters of fuzzy-neural networks is proposed for function approximation and adaptive control of nonlinear systems. In adaptive control procedure for nonlinear systems, weights of the fuzzy neural controller are online adjusted by the compact ant algorithm in order to generate appropriate control input. For the purpose of evaluating the stability of the closed-loop systems, an energy fitness function is used in the ant algorithm. Finally, a computer simulation example demonstrates the feasibility and effectiveness of the proposed method.

Keywords

MIMO systems; adaptive control; closed loop systems; function approximation; fuzzy neural nets; neurocontrollers; nonlinear control systems; optimisation; MIMO nonlinear system; adaptive nonlinear control system; ant colony optimization algorithm; closed loop system; energy fitness function; function approximation; fuzzy neural controller; fuzzy neural network backstepping controller; parameter tuning; stability evaluation; Annealing; Chaos; Routing; adaptive control; ant colony algorithm; fuzzy neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science and Engineering (ICSSE), 2010 International Conference on

Conference_Location

Taipei

Print_ISBN

978-1-4244-6472-2

Type

conf

DOI

10.1109/ICSSE.2010.5551754

Filename

5551754