Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input

Beibei Ren, Shuzhi Sam Ge, Chun Yi Su, Tong Heng Lee

Research output: Contribution to journalArticle

179 Scopus citations

Abstract

In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

Original languageEnglish
Pages (from-to)431-443
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume39
Issue number2
DOIs
StatePublished - 2009

Keywords

  • Adaptive control
  • Hysteresis
  • Neural networks (NNs)
  • Nonlinear systems
  • Pure-feedback

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