Sliding mode control of MEMS gyroscopes using composite learning

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 an...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2018-01, Vol.275, p.2555-2564
Main Authors: Zhang, Rui, Shao, Tianyi, Zhao, Wanliang, Li, Aijun, Xu, Bin
Format: Article
Language:English
Subjects:
Composite learning
MEMS gyroscopes
Neural network
Serial-parallel estimation model
Sliding mode control
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.11.032