Adaptive control of robot manipulator RBF network based on deadzone compensator. Lewis et al. [2] used backpropagation neural network to adaptive compensate deadzone in control system for manipulator neural-control. [3] presents a compensation scheme for general actuator nonlinearities. Compensator uses two neural networks, one to estimate unknown actuator nonlinearities and another to provide adaptive compensation in feedforward path. Compensator uses RBF neural networks to estimate the actuator nonlinearities and eliminate their effects. Give weight turning algorithms of RBF neural networks. The whole scheme provides a general procedure for using RBF neural networks to compensate the actuator nonlinearities in robot control system. reference: [1] Liu JinKun. Robot Control System Design and MATLAB Simulation[M]. Tsinghua University Press, 2008. [2] Lewis F L, Campos J, Selmic R. Neuro-fuzzy control of industrial systems with actuator nonlinearities[M]. Society for Industrial and Applied Mathematics, 2002. [3] Lu Y, Liu J K, Sun F C. Actuator nonlinearities compensation using RBF neural networks in robot control system[C]//The Proceedings of the Multiconference on" Computational Engineering in Systems Applications". IEEE, 2006, 1: 231-238. link: https://shi.buaa.edu.cn/liujinkun/zh_CN/jxzy/8049/list/index.htm