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 journalArticlepeer-review

93 Citations (Scopus)


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
Issue number7
Publication statusPublished - 2009

Bibliographical note

Funding Information:
Manuscript received December 03, 2007; revised June 13, 2008, September 25, 2008, and February 25, 2009; accepted April 24, 2009. First published May 15, 2009; current version published July 09, 2009. This work was supported in part by the Postdoctorate Research Program at Yonsei University and in part by Yonsei University Institute of TMS Information Technology under a Brain Korea 21 program.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


Dive into the research topics of 'Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays'. Together they form a unique fingerprint.

Cite this