Adaptive global sliding mode control for MEMS gyroscope using RBF neural network
type: Item
instance of: scholarly article
title: Adaptive global sliding mode control for MEMS gyroscope using RBF neural network
zbMATH Open document ID: 1394.93045
DOI: 10.1155/2015/403180
author: Y. D. Chu
author: Juntao Fei
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: An adaptive global sliding mode control (AGSMC) using RBF neural network (RBFNN) is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS) gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
Mathematics Subject Classification ID: 93B12
Mathematics Subject Classification ID: 70E05
zbMATH DE Number: 6926270
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/403180
OpenAlex ID: W2073830057
Wikidata QID: Q59118479
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description: scientific article; zbMATH DE number 6926270