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

doi:10.1109/ICSSE.2010.5551754

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

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2010 International Conference on System Science and Engineering, ICSSE 2010
Pages	146-149
Number of pages	4
DOIs	https://doi.org/10.1109/ICSSE.2010.5551754
Publication status	Published - 2010
Event	2010 International Conference on System Science and Engineering, ICSSE 2010 - Taipei, Taiwan Duration: 2010 Jul 1 → 2010 Jul 3

Publication series

Name	2010 International Conference on System Science and Engineering, ICSSE 2010

Other

Other	2010 International Conference on System Science and Engineering, ICSSE 2010
Country/Territory	Taiwan
City	Taipei
Period	2010/07/01 → 2010/07/03

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

Access to Document

10.1109/ICSSE.2010.5551754

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). Article 5551754 (2010 International Conference on System Science and Engineering, ICSSE 2010). https://doi.org/10.1109/ICSSE.2010.5551754

Compact ant colony optimization algorithm based fuzzy neural network backstepping controller for MIMO nonlinear systems. / Chen, Chao Kuang; Leu, Yih Guang ; Wang, Wei Yen et al.
2010 International Conference on System Science and Engineering, ICSSE 2010. 2010. p. 146-149 5551754 (2010 International Conference on System Science and Engineering, ICSSE 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, CK, Leu, YG , Wang, WY & Chen, CY 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., 5551754, 2010 International Conference on System Science and Engineering, ICSSE 2010, pp. 146-149, 2010 International Conference on System Science and Engineering, ICSSE 2010, Taipei, Taiwan, 2010/07/01. https://doi.org/10.1109/ICSSE.2010.5551754

Chen CK, Leu YG , Wang WY, Chen CY. 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. 2010. p. 146-149. 5551754. (2010 International Conference on System Science and Engineering, ICSSE 2010). doi: 10.1109/ICSSE.2010.5551754

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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.",

keywords = "Adaptive control, Ant colony algorithm, Fuzzy neural networks",

author = "Chen, {Chao Kuang} and Leu, {Yih Guang} and Wang, {Wei Yen} and Chen, {Chun Yao}",

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PY - 2010

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N2 - 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.

AB - 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.

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

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