DocumentCode :
635072
Title :
A combined backstepping and wavelet neural network control approach for mechanical system
Author :
Chiung-Chou Liao ; Chiu-Hsiung Chen ; Ya-Fu Peng ; Sung-Chi Wu
Author_Institution :
Dept. of Electron. Eng., Chien Hsin Univ. of Sci. & Technol., Jhongli, Taiwan
fYear :
2013
fDate :
23-26 June 2013
Firstpage :
1
Lastpage :
6
Abstract :
A combined backstepping and wavelet neural network control approach for mechanical system is proposed in this paper. The proposed control approach comprises a neural controller and a robust compensator. The neural controller using a wavelet neural network (WNN) is the main controller based on backstepping method; and the parameters of WNN are on-line tune by adaptation laws from the Lyapunov stability theorem. The robust compensator is designed to dispel the approximation error, so the asymptotic stability of the system can be guaranteed. Finally, a mass-spring-damper system is performed to verify the effectiveness of the proposed control scheme.
Keywords :
Lyapunov methods; adaptive control; approximation theory; asymptotic stability; compensation; neurocontrollers; shock absorbers; springs (mechanical); vibration control; wavelet transforms; Lyapunov stability theorem; WNN; adaptation laws; adaptive control; approximation error; asymptotic stability; backstepping control approach; mass-spring-damper system; mechanical system; neural controller; robust compensator; wavelet neural network control approach; Backstepping; Control systems; Lyapunov methods; Mechanical systems; Neural networks; Robustness; Uncertainty; adaptive control; backstepping control; wavelet neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ASCC), 2013 9th Asian
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-5767-8
Type :
conf
DOI :
10.1109/ASCC.2013.6606219
Filename :
6606219
Link To Document :
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