This paper proposed a novel adaptive tracking neural network with deadzone robust compensator for Industrial Robot Manipulators (IRMs) to achieve the high precision position tracking performance. In order, to deal the uncertainty, the unknown deadzone effect, the unknown dynamics, and disturbances of robot system, the Radial Basis function neural networks (RBFNNs) control is presented to control the joint position and approximate the unknown dynamics of an n-link robot manipulator. The online adaptive control training laws and estimation of the dead-zone are determined by Lyapunov stability and the approximation theory, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this controller, a robust compensator is constructed as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances and modeling uncertainties. The proposed control is the verified on a three-joint robot manipulators via simulations and experiments in comparison with PID and Neural networks (NNs) control.

中文翻译：

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具有死区鲁棒补偿器的工业机器人机械手的自适应轨迹神经网络跟踪控制

本文提出了一种用于工业机器人机械手（IRM）的具有死区鲁棒补偿器的新型自适应跟踪神经网络，以实现高精度的位置跟踪性能。为了处理机器人系统的不确定性、未知死区效应、未知动力学和扰动，提出径向基函数神经网络（RBFNNs）控制来控制关节位置并逼近n-link的未知动力学机器人操纵器。在线自适应控制训练规律和死区估计由李雅普诺夫稳定性和近似理论确定，从而保证了整个系统的稳定性和权重自适应的收敛性。在这个控制器中，构建鲁棒补偿器作为辅助控制器，以保证质量变化、外部扰动和建模不确定性等各种环境下的稳定性和鲁棒性。与 PID 和神经网络 (NN) 控制相比，通过仿真和实验在三关节机器人操纵器上验证了所提出的控制。