Robust Adaptive Neural Networks with an Online Learning Technique for Robot Control

  • Zhi-gang Yu
  • Shen-min Song
  • Guang-ren Duan
  • Run Pei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


A new robust adaptive neural networks tracking control with online learning controller is proposed for robot systems. A learning strategy and robust adaptive neural networks are combined into a hybrid robust control scheme. The proposed controller deals mainly with external disturbances and nonlinear uncertainty in motion control. A neural network (NN) is used to approximate the uncertainties in a robotic system. Then the disadvantageous effects on tracking performance, due to the approximating error of the NN in robotic system, are attenuated to a prescribed level by an adaptive robust controller. The learning techniques of NN will improve robustness with respect to uncertainty of system, as a result, improving the dynamic performance of robot system. A simulation example demonstrates the effectiveness of the proposed control strategy.


Tracking Error Radial Basis Function Neural Network Robot System Adaptive Robust Controller Nonlinear Uncertainty 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhi-gang Yu
    • 1
  • Shen-min Song
    • 1
  • Guang-ren Duan
    • 1
  • Run Pei
    • 1
  1. 1.School of AerospaceHarbin Institute of TechnologyHarbinChina

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