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

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

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  • 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