Neural adaptive control for a class of nonlinear systems with unknown deadzone

  • Authors:

  • Zhonghua Wang;Yong Zhang;Hui Fang

  • Affiliations:

  • University of Jinan, School of Control Science and Engineering, 250022, Jinan, People’s Republic of China;University of Jinan, School of Control Science and Engineering, 250022, Jinan, People’s Republic of China;University of Jinan, School of Control Science and Engineering, 250022, Jinan, People’s Republic of China

  • Venue:
  • Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
  • Year:
  • 2008

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Abstract

This paper focuses on the adaptive control of a class of nonlinear systems with unknown deadzone using neural networks. By constructing a deadzone pre-compensator, a neural adaptive control scheme is developed using backstepping design techniques. Transient performance is guaranteed and semi-globally uniformly ultimately bounded stability is obtained. Another feature of this scheme is that the neural networks reconstruction error bound is assumed to be unknown and can be estimated online. Simulation results are given to demonstrate the effectiveness of the proposed controller.