Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chu, Yundi& Fei, Juntao. 2015. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073739
Modern Language Association (MLA)
Chu, Yundi& Fei, Juntao. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1073739
American Medical Association (AMA)
Chu, Yundi& Fei, Juntao. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073739
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073739