DocumentCode :
575481
Title :
Adaptive neural sliding mode control of MEMS triaxial gyroscope based on feedback linearization approach
Author :
Fei, Juntao ; Ding, Hongfei
Author_Institution :
Jiangsu Key Lab. of Power Transm. & Distrib. Equip. Technol., Hohai Univ., Changzhou, China
fYear :
2012
fDate :
20-23 Aug. 2012
Firstpage :
1271
Lastpage :
1276
Abstract :
In this paper, a neural network adaptive sliding mode control is proposed for the MEMS triaixal gyroscope with unknown system nonlinearities. An input-output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities and the neural network whose parameters are updated from the Lyapunov approach is used to perform the linearization control law. Sliding mode control is utilized to compensate the neural network approximation errors. The stability of the closed-loop system can be guaranteed with the proposed adaptive neural sliding mode control. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme.
Keywords :
Lyapunov methods; adaptive control; approximation theory; closed loop systems; control nonlinearities; gyroscopes; linearisation techniques; microsensors; neurocontrollers; numerical analysis; variable structure systems; Lyapunov approach; MEMS triaxial gyroscope; adaptive neural sliding mode control; closed-loop system stability; control nonlinearity; feedback linearization approach; input-output linearization technique; linearization control law; neural adaptive tracking control; neural network adaptive sliding mode control; neural network approximation errors; numerical simulations; unknown system nonlinearity; Adaptive systems; Approximation methods; Biological neural networks; Gyroscopes; Micromechanical devices; Sliding mode control; Adaptive Control; Neural Network; Robust Control; Sliding Mode Control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SICE Annual Conference (SICE), 2012 Proceedings of
Conference_Location :
Akita
ISSN :
pending
Print_ISBN :
978-1-4673-2259-1
Type :
conf
Filename :
6318642
Link To Document :
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