Sliding mode control of MEMS gyroscopes using composite learning

Rui Zhang, Tianyi Shao, Wanliang Zhao, Aijun Li, Bin Xu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

This paper investigates the sliding mode control with composite learning for MEMS Gyroscopes, which not only focuses on the system tracking and stability analysis, but also pays close attention to the accuracy of desired identified uncertain dynamics. The serial–parallel estimation model is given and a filter error included tracking error and modeling error is constructed to design the weights updating law of neural networks (NNs). Simulation results demonstrate that the proposed approach achieves better tracking performance with higher accuracy.

Original languageEnglish
Pages (from-to)2555-2564
Number of pages10
JournalNeurocomputing
Volume275
DOIs
StatePublished - 31 Jan 2018

Keywords

  • Composite learning
  • MEMS gyroscopes
  • Neural network
  • Serial-parallel estimation model
  • Sliding mode control

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