Robust Adaptive Neural Control for a Class of Time-Varying Delay Systems with Backlash-like Hysteresis Input

Xiuyu Zhang, Zhi Li, Chun Yi Su, Xinkai Chen, Jianguo Wang, Linlin Xia

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper proposes a robust adaptive dynamic surface control (DSC) scheme for a class of time-varying delay systems with backlash-like hysteresis input. The main features of the proposed DSC method are that 1) by using a transformation function, the prescribed transient performance of the tracking error can be guaranteed; 2) by estimating the norm of the unknown weighted vector of the neural network, the computational burden can be greatly reduced; 3) by using the DSC method, the explosion of complexity problem is eliminated. It is proved that the proposed scheme guarantees all the closed-loop signals being uniformly ultimately bounded. The simulation results show the validity of the proposed control scheme.

Original languageEnglish
JournalAsian Journal of Control
DOIs
Publication statusAccepted/In press - 2015

Fingerprint

Control surfaces
Hysteresis
Explosions
Neural networks

Keywords

  • Backlash-like hysteresis
  • Dynamic surface control
  • Prescribed tracking error performance
  • Unknown time-varying delay

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Robust Adaptive Neural Control for a Class of Time-Varying Delay Systems with Backlash-like Hysteresis Input. / Zhang, Xiuyu; Li, Zhi; Su, Chun Yi; Chen, Xinkai; Wang, Jianguo; Xia, Linlin.

In: Asian Journal of Control, 2015.

Research output: Contribution to journalArticle

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AU - Xia, Linlin

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AB - This paper proposes a robust adaptive dynamic surface control (DSC) scheme for a class of time-varying delay systems with backlash-like hysteresis input. The main features of the proposed DSC method are that 1) by using a transformation function, the prescribed transient performance of the tracking error can be guaranteed; 2) by estimating the norm of the unknown weighted vector of the neural network, the computational burden can be greatly reduced; 3) by using the DSC method, the explosion of complexity problem is eliminated. It is proved that the proposed scheme guarantees all the closed-loop signals being uniformly ultimately bounded. The simulation results show the validity of the proposed control scheme.

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