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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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 c1osed-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.

Original languageEnglish
Title of host publication2010 International Conference on System Science and Engineering, ICSSE 2010
Pages146-149
Number of pages4
DOIs
Publication statusPublished - 2010 Oct 11
Event2010 International Conference on System Science and Engineering, ICSSE 2010 - Taipei, Taiwan
Duration: 2010 Jul 12010 Jul 3

Publication series

Name2010 International Conference on System Science and Engineering, ICSSE 2010

Other

Other2010 International Conference on System Science and Engineering, ICSSE 2010
CountryTaiwan
CityTaipei
Period10/7/110/7/3

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Keywords

  • Adaptive control
  • Ant colony algorithm
  • Fuzzy neural networks

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Control and Systems Engineering

Cite this

Chen, C. K., Leu, Y. G., Wang, W. Y., & Chen, C. Y. (2010). Compact ant colony optimization algorithm based fuzzy neural network backstepping controller for MIMO nonlinear systems. In 2010 International Conference on System Science and Engineering, ICSSE 2010 (pp. 146-149). [5551754] (2010 International Conference on System Science and Engineering, ICSSE 2010). https://doi.org/10.1109/ICSSE.2010.5551754