Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems

I. Hsum Li; Lian Wang Lee; Wei Yen Wang

doi:10.1109/IJCNN.2012.6252454

Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems

I. Hsum Li^*, Lian Wang Lee, Wei Yen Wang

^*Corresponding author for this work

Department of Electrical Engineering

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

Abstract

In this paper, we propose a hybrid adaptive control scheme based on Hopfield-based dynamic neural network (HACHNN) for SISO nonlinear systems. An auxiliary direct adaptive controller is proposed to ensure the stability in the time-interval of when an indirect adaptive controller is failed because of ĝ(x)→0. The weights of the Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov theorem, so that the stability of the closed-loop system can be guaranteed, and all signals in the closed-loop system are bounded. The designed structure of the Hopfield-based dynamic neural network maintains the tracking performance of the control scheme, and it also makes the practical implementation much easier.

Original language	English
Title of host publication	2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs	https://doi.org/10.1109/IJCNN.2012.6252454
Publication status	Published - 2012
Event	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia Duration: 2012 Jun 10 → 2012 Jun 15

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	2012/06/10 → 2012/06/15

Keywords

Hopfield dynamic neural network
hybrid adaptive control scheme

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2012.6252454

Cite this

Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems. / Li, I. Hsum; Lee, Lian Wang; Wang, Wei Yen.
2012 International Joint Conference on Neural Networks, IJCNN 2012. 2012. 6252454 (Proceedings of the International Joint Conference on Neural Networks).

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

Li, IH, Lee, LW & Wang, WY 2012, Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems. in 2012 International Joint Conference on Neural Networks, IJCNN 2012., 6252454, Proceedings of the International Joint Conference on Neural Networks, 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, QLD, Australia, 2012/06/10. https://doi.org/10.1109/IJCNN.2012.6252454

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title = "Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems",

abstract = "In this paper, we propose a hybrid adaptive control scheme based on Hopfield-based dynamic neural network (HACHNN) for SISO nonlinear systems. An auxiliary direct adaptive controller is proposed to ensure the stability in the time-interval of when an indirect adaptive controller is failed because of ĝ(x)→0. The weights of the Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov theorem, so that the stability of the closed-loop system can be guaranteed, and all signals in the closed-loop system are bounded. The designed structure of the Hopfield-based dynamic neural network maintains the tracking performance of the control scheme, and it also makes the practical implementation much easier.",

keywords = "Hopfield dynamic neural network, hybrid adaptive control scheme",

author = "Li, {I. Hsum} and Lee, {Lian Wang} and Wang, {Wei Yen}",

year = "2012",

doi = "10.1109/IJCNN.2012.6252454",

language = "English",

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series = "Proceedings of the International Joint Conference on Neural Networks",

booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",

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AU - Li, I. Hsum

AU - Lee, Lian Wang

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N2 - In this paper, we propose a hybrid adaptive control scheme based on Hopfield-based dynamic neural network (HACHNN) for SISO nonlinear systems. An auxiliary direct adaptive controller is proposed to ensure the stability in the time-interval of when an indirect adaptive controller is failed because of ĝ(x)→0. The weights of the Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov theorem, so that the stability of the closed-loop system can be guaranteed, and all signals in the closed-loop system are bounded. The designed structure of the Hopfield-based dynamic neural network maintains the tracking performance of the control scheme, and it also makes the practical implementation much easier.

AB - In this paper, we propose a hybrid adaptive control scheme based on Hopfield-based dynamic neural network (HACHNN) for SISO nonlinear systems. An auxiliary direct adaptive controller is proposed to ensure the stability in the time-interval of when an indirect adaptive controller is failed because of ĝ(x)→0. The weights of the Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov theorem, so that the stability of the closed-loop system can be guaranteed, and all signals in the closed-loop system are bounded. The designed structure of the Hopfield-based dynamic neural network maintains the tracking performance of the control scheme, and it also makes the practical implementation much easier.

KW - Hopfield dynamic neural network

KW - hybrid adaptive control scheme

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Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems

Abstract

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Keywords

ASJC Scopus subject areas

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