Composite Neural Learning-Based Nonsingular Terminal Sliding Mode Control of MEMS Gyroscopes

Bin Xu, Rui Zhang, Shuai Li, Wei He, Zhongke Shi

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

Abstract

The efficient driving control of MEMS gyroscopes is an attractive way to improve the precision without hardware redesign. This paper investigates the sliding mode control (SMC) for the dynamics of MEMS gyroscopes using neural networks (NNs). Considering the existence of the dynamics uncertainty, the composite neural learning is constructed to obtain higher tracking precision using the serial-parallel estimation model (SPEM). Furthermore, the nonsingular terminal SMC (NTSMC) is proposed to achieve finite-time convergence. To obtain the prescribed performance, a time-varying barrier Lyapunov function (BLF) is introduced to the control scheme. Through simulation tests, it is observed that under the BLF-based NTSMC with composite learning design, the tracking precision of MEMS gyroscopes is highly improved.

Original languageEnglish
Article number8746811
Pages (from-to)1375-1386
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number4
DOIs
StatePublished - Apr 2020

Keywords

  • Composite learning
  • MEMS gyroscopes
  • neural network
  • nonsingular terminal sliding mode control
  • time-varying barrier Lyapunov function

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