Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants

Muhammad Saleheen Aftab, Muhammad Shafiq, Hasan Yousef

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

3 Citations (Scopus)

Abstract

This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Industrial Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages321-325
Number of pages5
Volume2015-June
EditionJune
DOIs
Publication statusPublished - Jun 16 2015
Event2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain
Duration: Mar 17 2015Mar 19 2015

Other

Other2015 IEEE International Conference on Industrial Technology, ICIT 2015
CountrySpain
CitySeville
Period3/17/153/19/15

Fingerprint

Stability criteria
Lyapunov functions
Controllers
Backpropagation algorithms
Feedforward neural networks
Learning algorithms
Dynamical systems
Neural networks

Keywords

  • direct adaptive inverse control
  • indirect adaptive inverse control
  • Lyapunov function
  • neural inverse tracking
  • stable adaptive tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

Cite this

Aftab, M. S., Shafiq, M., & Yousef, H. (2015). Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. In Proceedings of the IEEE International Conference on Industrial Technology (June ed., Vol. 2015-June, pp. 321-325). [7125118] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2015.7125118

Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. / Aftab, Muhammad Saleheen; Shafiq, Muhammad; Yousef, Hasan.

Proceedings of the IEEE International Conference on Industrial Technology. Vol. 2015-June June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 321-325 7125118.

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

Aftab, MS, Shafiq, M & Yousef, H 2015, Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. in Proceedings of the IEEE International Conference on Industrial Technology. June edn, vol. 2015-June, 7125118, Institute of Electrical and Electronics Engineers Inc., pp. 321-325, 2015 IEEE International Conference on Industrial Technology, ICIT 2015, Seville, Spain, 3/17/15. https://doi.org/10.1109/ICIT.2015.7125118
Aftab MS, Shafiq M, Yousef H. Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. In Proceedings of the IEEE International Conference on Industrial Technology. June ed. Vol. 2015-June. Institute of Electrical and Electronics Engineers Inc. 2015. p. 321-325. 7125118 https://doi.org/10.1109/ICIT.2015.7125118
Aftab, Muhammad Saleheen ; Shafiq, Muhammad ; Yousef, Hasan. / Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants. Proceedings of the IEEE International Conference on Industrial Technology. Vol. 2015-June June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 321-325
@inproceedings{a62a91a30a9049f6a69a5d9484108b4a,
title = "Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants",
abstract = "This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.",
keywords = "direct adaptive inverse control, indirect adaptive inverse control, Lyapunov function, neural inverse tracking, stable adaptive tracking",
author = "Aftab, {Muhammad Saleheen} and Muhammad Shafiq and Hasan Yousef",
year = "2015",
month = "6",
day = "16",
doi = "10.1109/ICIT.2015.7125118",
language = "English",
volume = "2015-June",
pages = "321--325",
booktitle = "Proceedings of the IEEE International Conference on Industrial Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
edition = "June",

}

TY - GEN

T1 - Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants

AU - Aftab, Muhammad Saleheen

AU - Shafiq, Muhammad

AU - Yousef, Hasan

PY - 2015/6/16

Y1 - 2015/6/16

N2 - This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.

AB - This paper presents a Lyapunov function based neural network tracking control strategy for single-input-single-output nonlinear dynamic systems. The proposed architecture is composed of two feed-forward neural networks operating as controller and estimator in a unified framework. The network parameters are tuned online with a Lyapunov function based backpropagation learning algorithm. The closed-loop error convergence and stability are analyzed with Lyapunov stability theory. Two simulation case studies are included that successfully validate the proposed controller performance.

KW - direct adaptive inverse control

KW - indirect adaptive inverse control

KW - Lyapunov function

KW - neural inverse tracking

KW - stable adaptive tracking

UR - http://www.scopus.com/inward/record.url?scp=84937716362&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84937716362&partnerID=8YFLogxK

U2 - 10.1109/ICIT.2015.7125118

DO - 10.1109/ICIT.2015.7125118

M3 - Conference contribution

VL - 2015-June

SP - 321

EP - 325

BT - Proceedings of the IEEE International Conference on Industrial Technology

PB - Institute of Electrical and Electronics Engineers Inc.

ER -