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

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

科研成果: 期刊稿件文章同行评审

90 引用 (Scopus)

摘要

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.

源语言英语
文章编号8746811
页(从-至)1375-1386
页数12
期刊IEEE Transactions on Neural Networks and Learning Systems
31
4
DOI
出版状态已出版 - 4月 2020

指纹

探究 'Composite Neural Learning-Based Nonsingular Terminal Sliding Mode Control of MEMS Gyroscopes' 的科研主题。它们共同构成独一无二的指纹。

引用此