College of electronic and Information Engineering, Taizhou University, Taizhou, Zhejiang, 318000, China.

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Abstract—Robot manipulators are subject to different types of uncertainties which may degrade the tracking control performance or even make the system unstable. In this paper, a neural network tracking controller with disturbance observer is developed to deal with both the external disturbance and the dynamic parametric uncertainties. First, RBF neural network is introduced to learn and approximate the uncertain robot dynamic by using adaptive algorithm. Next, a nonlinear disturbance observer is designed to estimate and compensate for external disturbances and remove the effect of the disturbance. Simulation results show that the proposed control scheme has good tracking performance, which can effectively suppress the uncertain dynamics and external disturbances of the manipulator systems.

Index Terms—Robot manipulators, disturbance observer, RBF neural network, uncertain dynamic.

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Cite: San-Xiu Wang, Guang Chen, Ling-Wei Wu, "Neural Network Control with Disturbance Observer for Uncertain Robot Manipulator," Journal of Computers vol. 14, no. 1, pp. 71-78, 2019.