• CN: 11-2187/TH
  • ISSN: 0577-6686

›› 2010, Vol. 46 ›› Issue (13): 68-75.

• Article • Previous Articles     Next Articles

Hybrid Control Using Wavelet Neural Networks and Robust Method for Flexible Joint with Friction and Uncertainties Compensation

SUN Hanxu;CHU Ming;JIA Qingxuan   

  1. Automation School, Beijing University of Posts and Telecommunications
  • Published:2010-07-05

Abstract: The high-precision position tracking control for flexible joint is researched. The second-order cascade dynamics equations of flexible joint are established; then, non-consecutive friction and external interference are introduced. In view of the complex dynamic character istics that flexibility coexists with friction, uncertain parameter perturbations and external disturbances, a hybrid control strategy is proposed. The global controller is designed by using Lyapunov function backstepping method to compensate the flexibility and suppress disturbance based on L2 property. In view of the problem that it is hard for the conventional neural networks to identify the non-consecutive function like friction, the wavelet neural networks approach is locally utilized to estimate the nonlinear terms including friction and uncertainties. This strategy can avoid the complicated derivation operation, joint angle acceleration measurement and upper bound forecast. The closed-loop stability analysis and numerical simulation show that the proposed controller has robust stability and disturbance attenuation performance. The tracking error and network weights error are uniformly ultimately bounded, and the crawling and flat top phenomena induced by friction are avoided.

Key words: Backstepping technique, Flexible joint, Hybrid control, Non-consecutive friction, Wavelet neural networks, continuous friction, fretting interfaces, fretting wear, molecular dynamics simulation

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