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Adaptive nonlinear velocity tracking using RBFNN for linear DC brushless motor

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  • Ching-Chih Tsai
  • Cheng-Kain Chan
  • Yi Yu Li

Abstract

This article presents an adaptive H∞ nonlinear velocity control for a linear DC brushless motor. A simplified model of this motor with friction is briefly recalled. The friction dynamics is described by the Lu Gre model and the online tuning radial basis function neural network (RBFNN) is used to parameterise the nonlinear friction function and un-modelled errors. An adaptive nonlinear H∞ control method is then proposed to achieve velocity tracking, by assuming that the upper bounds of the ripple force, the changeable load and the nonlinear friction can be learned by the RBFNN. The closed-loop system is proven to be uniformly bounded using the Lyapunov stability theory. The feasibility and the efficacy of the proposed control are exemplified by conducting two velocity tracking experiments.

Suggested Citation

  • Ching-Chih Tsai & Cheng-Kain Chan & Yi Yu Li, 2011. "Adaptive nonlinear velocity tracking using RBFNN for linear DC brushless motor," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(1), pages 63-72.
    • Handle: RePEc:taf:tsysxx:v:43:y:2011:i:1:p:63-72
    DOI: 10.1080/00207721003768183
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