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

Abstract

AbstractA 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 ErrorRadial Basis Function Neural NetworkRobot SystemAdaptive Robust ControllerNonlinear UncertaintyThese 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.