Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays

Sung Jin Yoo, Jin Bae Park, Yoon Ho Choi

Research output: Contribution to journalArticle

67 Citations (Scopus)

Abstract

This brief proposes a simple control approach for a class of uncertain nonlinear systems with unknown time delays in strict-feedback form. That is, the dynamic surface control technique, which can solve the "explosion of complexity" problem in the backstepping design procedure, is extended to nonlinear systems with unknown time delays. The unknown time-delay effects are removed by using appropriate Lyapunov-Krasovskii functionals, and the uncertain nonlinear terms generated by this procedure as well as model uncertainties are approximated by the function approximation technique using neural networks. In addition, the bounds of external disturbances are estimated by the adaptive technique. From the Lyapunov stability theorem, we prove that all signals in the closed-loop system are semiglobally uniformly bounded. Finally, we present simulation results to validate the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)1209-1215
Number of pages7
JournalIEEE Transactions on Neural Networks
Volume20
Issue number7
DOIs
Publication statusPublished - 2009 Aug 5

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Nonlinear systems
Time delay
Feedback
Backstepping
Explosions
Control surfaces
Closed loop systems
Uncertainty
Neural networks

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Medicine(all)

Cite this

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Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays. / Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho.

In: IEEE Transactions on Neural Networks, Vol. 20, No. 7, 05.08.2009, p. 1209-1215.

Research output: Contribution to journalArticle

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