Observer-based adaptive neural optimal control for discrete-time systems in nonstrict-feedback form

Shiyi Zhao, Hongjing Liang, Choon Ki Ahn, Peihao Du

Research output: Contribution to journalArticle

Abstract

This paper considers the problem of observer-based adaptive near-optimal control for a class of nonstrict-feedback discrete-time nonlinear systems with non-symmetric dead zone. In order to compensate the effect of dead-zone on the control performance, an adaptive auxiliary signal is constructed to estimate the unknown dead-zone parameters. For the unknown nonlinear functions, neural networks (NNs)are introduced to identify them and to estimate the unknown parameters. To resolve the difficulty resulting from the unavailable state variables, an NN-based observer is designed. Moreover, according to the framework of adaptive control, a novel reinforcement learning algorithm is developed to guarantee that the near-optimal control performance is achieved. Finally, some simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • Adaptive near-optimal control
  • Backstepping control
  • Dead-zone input
  • Nonstrict-feedback nonlinear system

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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