Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems

Ping Cheng Chen; Chi-Hsu Wang; Tsu Tian Lee

doi:10.1007/978-3-642-03969-0_31

Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems

Ping Cheng Chen^*, 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

Abstract

In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. 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. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. 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	Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings
Pages	336-349
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-642-03969-0_31
State	Published - 1 Dec 2009
Event	11th International Conference on Engineering Applications of Neural Networks, EANN 2009 - London, United Kingdom Duration: 27 Aug 2009 → 29 Aug 2009

Publication series

Name	Communications in Computer and Information Science
Volume	43 CCIS
ISSN (Print)	1865-0929

Conference

Conference	11th International Conference on Engineering Applications of Neural Networks, EANN 2009
Country	United Kingdom
City	London
Period	27/08/09 → 29/08/09

Keywords

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

Access to Document

10.1007/978-3-642-03969-0_31

Fingerprint Dive into the research topics of 'Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems'. Together they form a unique fingerprint.

Cite this

Chen, P. C., Wang, C-H., & Lee, T. T. (2009). Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems. In Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings (pp. 336-349). (Communications in Computer and Information Science; Vol. 43 CCIS). https://doi.org/10.1007/978-3-642-03969-0_31

@inproceedings{5b4ead8915724ef2867f0be0bc0dddca,

title = "Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems",

abstract = "In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. 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. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. 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 = "dynamic neural network, Hopfield-based dynamic neural network, indirect adaptive control, Lyapunov stability theory",

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

year = "2009",

month = dec,

day = "1",

doi = "10.1007/978-3-642-03969-0_31",

language = "English",

isbn = "3642039685",

series = "Communications in Computer and Information Science",

pages = "336--349",

booktitle = "Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings",

note = "null ; Conference date: 27-08-2009 Through 29-08-2009",

}

Chen, PC, Wang, C-H & Lee, TT 2009, Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems. in Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings. Communications in Computer and Information Science, vol. 43 CCIS, pp. 336-349, 11th International Conference on Engineering Applications of Neural Networks, EANN 2009, London, United Kingdom, 27/08/09. https://doi.org/10.1007/978-3-642-03969-0_31

Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems. / Chen, Ping Cheng; Wang, Chi-Hsu; Lee, Tsu Tian.

Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings. 2009. p. 336-349 (Communications in Computer and Information Science; Vol. 43 CCIS).

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

TY - GEN

T1 - Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems

AU - Chen, Ping Cheng

AU - Wang, Chi-Hsu

AU - Lee, Tsu Tian

PY - 2009/12/1

Y1 - 2009/12/1

N2 - In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. 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. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. 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 an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. 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. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. 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.

KW - dynamic neural network

KW - Hopfield-based dynamic neural network

KW - indirect adaptive control

KW - Lyapunov stability theory

UR - http://www.scopus.com/inward/record.url?scp=78049358144&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-03969-0_31

DO - 10.1007/978-3-642-03969-0_31

M3 - Conference contribution

AN - SCOPUS:78049358144

SN - 3642039685

SN - 9783642039683

T3 - Communications in Computer and Information Science

SP - 336

EP - 349

BT - Engineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings

Y2 - 27 August 2009 through 29 August 2009

ER -