• 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