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Compensating Modeling and Control for Friction Using RBF Adaptive Neural Networks

  • Yongfu Wang
  • Tianyou Chai
  • Lijie Zhao
  • Ming Tie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

This paper presents an application of a radial basis functions adaptive neural networks for compensating the effects induced by the friction in mechanical system. An adaptive neural networks based on radial basis functions is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The hybrid controller is a combination of a PD+G controller and a neural networks controller which compensates for nonlinear friction. The proposed scheme is simulated on a single link robot control system. The algorithm and simulations results are described.

Keywords

Radial Basis Function Tracking Error Friction Model Basis Function Neural Network Radial Neural Network Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yongfu Wang
    • 1
  • Tianyou Chai
    • 1
  • Lijie Zhao
    • 1
  • Ming Tie
    • 1
  1. 1.Research Center of AutomationNortheastern UniversityShenyangChina

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