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An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

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  • Yuxiang Wang
  • Zhangwei Chen
  • Hongfei Zu
  • Xiang Zhang

Abstract

Friction is a nonlinear effect that occurs in all mechanical systems which may cause limit cycles, tracking errors, and other undesirable effects. Traditional static friction models cannot characterize all the friction situations. In recent years, neural network (NN) technique has been widely used to approximate the nonlinear function. In this paper, a new method which combines radical basis function neural network (BRFNN) with beetle antennae search (BAS) algorithm for modeling friction in a robotic joint is proposed. Velocity, load, and temperature are considered as the three factors that influence the static friction. It is shown that the proposed BAS-RBFNN possesses better performance in terms of faster convergence rate and higher accuracy.

Suggested Citation

  • Yuxiang Wang & Zhangwei Chen & Hongfei Zu & Xiang Zhang, 2020. "An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, February.
    • Handle: RePEc:hin:jnlmpe:5839195
    DOI: 10.1155/2020/5839195
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