Adaptive robust finite-time neural control of uncertain PMSM servo system with nonlinear dead zone

Qiang Chen*, Xuemei Ren, Jing Na, Dongdong Zheng

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

51 Citations (Scopus)

Abstract

In this paper, an adaptive robust finite-time neural control scheme is proposed for uncertain permanent magnet synchronous motor servo system with nonlinear dead-zone input. According to the differential mean value theorem, the dead zone is represented as a linear time-varying system, and the model uncertainty including the dead zone is approximated by using a simple neural network. Then, an adaptive finite-time controller is designed based on a fast terminal sliding mode control principle, and the singularity problem in the initial TSMC is circumvented by modifying the terminal sliding manifold. Comparative experiments are conducted to validate the effectiveness and superior performance of the proposed method.

Original languageEnglish
Number of pages12
JournalNeural Computing and Applications
Early online date19 Mar 2016
DOIs
Publication statusE-pub ahead of print - 19 Mar 2016

Keywords

  • Adaptive control
  • Dead zone
  • Finite-time control
  • Neural network
  • Servo system

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