Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form

Bin Xu, Zhongke Shi, Chenguang Yang, Fuchun Sun

Research output: Contribution to journalArticlepeer-review

368 Scopus citations

Abstract

This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals and their derivatives. Different from directly toward the asymptotic tracking, the accuracy of the identified neural models is taken into consideration. The prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the composite laws for NN weights updating. The uniformly ultimate boundedness stability is established using Lyapunov method. Simulation results are presented to demonstrate that the proposed method achieves smoother parameter adaption, better accuracy, and improved performance.

Original languageEnglish
Article number6783745
Pages (from-to)2626-2634
Number of pages9
JournalIEEE Transactions on Cybernetics
Volume44
Issue number12
DOIs
StatePublished - 1 Dec 2014

Keywords

  • Composite control
  • Dynamic surface control
  • Neural network
  • Serial-parallel estimation model
  • Strict-feedback

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