Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems

Pin Cheng Chen; Ping Zing Lin; Chi-Hsu Wang; Tsu Tian Lee

doi:10.1007/978-3-642-13318-3_62

Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems

Pin Cheng Chen^*, Ping Zing Lin, Chi-Hsu Wang, Tsu Tian Lee

^*Corresponding author for this work

Department of Electrical and Computer Engineering

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

2 Scopus citations

Abstract

In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L₂ tracking performance to stabilize the closed-loop system. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings
Pages	497-506
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-13318-3_62
State	Published - 14 Jul 2010
Event	7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China Duration: 6 Jun 2010 → 9 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6064 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Symposium on Neural Networks, ISNN 2010
Country	China
City	Shanghai
Period	6/06/10 → 9/06/10

Keywords

adaptive control
Hopfield-based dynamic neural network
Lyapunov stability theory
robust control

Access to Document

10.1007/978-3-642-13318-3_62

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Chen, P. C., Lin, P. Z., Wang, C-H., & Lee, T. T. (2010). Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems. In Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings (PART 2 ed., pp. 497-506). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6064 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-13318-3_62

Chen, Pin Cheng ; Lin, Ping Zing ; Wang, Chi-Hsu ; Lee, Tsu Tian. / Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems. Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings. PART 2. ed. 2010. pp. 497-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L2 tracking performance to stabilize the closed-loop system. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.",

keywords = "adaptive control, Hopfield-based dynamic neural network, Lyapunov stability theory, robust control",

author = "Chen, {Pin Cheng} and Lin, {Ping Zing} and Chi-Hsu Wang and Lee, {Tsu Tian}",

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Chen, PC, Lin, PZ, Wang, C-H & Lee, TT 2010, Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems. in Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6064 LNCS, pp. 497-506, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, 6/06/10. https://doi.org/10.1007/978-3-642-13318-3_62

Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems. / Chen, Pin Cheng; Lin, Ping Zing; Wang, Chi-Hsu; Lee, Tsu Tian.

Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings. PART 2. ed. 2010. p. 497-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6064 LNCS, No. PART 2).

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

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N2 - In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L2 tracking performance to stabilize the closed-loop system. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.

AB - In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L2 tracking performance to stabilize the closed-loop system. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.

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Chen PC, Lin PZ, Wang C-H, Lee TT. Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems. In Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings. PART 2 ed. 2010. p. 497-506. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-13318-3_62