Robust Adaptive Neural Networks with an Online Learning Technique for Robot Control
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.
KeywordsTracking Error Radial Basis Function Neural Network Robot System Adaptive Robust Controller Nonlinear Uncertainty
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- 5.Bai, P., Fang, T.J., Ge, Y.J.: Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control. Control Theory and Applications 18(6), 895–903 (2001)Google Scholar