Adaptive Neural Network Robust Control for Space Robot with Uncertainty

Zhang Wenhui, Fang Yamin, Ye Xiaoping Ye Xiaoping

Abstract


The trajectory tracking problems of a class of space robot manipulators with parameters and non-parameters uncertainty are considered. An adaptive robust control algorithm based on neural network is proposed by the paper. Neutral network is used to adaptive learn and compensate the unknown system for parameters uncertainties, the weight adaptive laws are designed by the paper, System stability base on Lyapunov theory is analysised to ensure the convergence of the algorithm. Non-parameters uncertainties are estimated and compensated by robust controller. It is proven that the designed controller can guarantee the asymptotic convergence of tracking error. The controller could guarantee good robust and the stability of closed-loop system. The simulation results show that the presented method is effective.


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References


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DOI: http://dx.doi.org/10.12928/telkomnika.v11i3.1130

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