Robust adaptive neural networks with an online learning technique for robot control

Authors:
Zhi-gang Yu;Shen-min Song;Guang-ren Duan;Run Pei
Affiliations:
School of Aerospace, Harbin Institute of Technology, Harbin, China;School of Aerospace, Harbin Institute of Technology, Harbin, China;School of Aerospace, Harbin Institute of Technology, Harbin, China;School of Aerospace, Harbin Institute of Technology, Harbin, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

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.