In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

中文翻译：

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改进的基于神经网络的动态不确定机械手自适应跟踪控制

在本文中，使用径向基函数神经网络为动态不确定的机器人机械手开发了一种鲁棒的自适应跟踪控制器。由于外部干扰及其动力学的不确定性，机器人机械手的跟踪控制系统的设计是一项极具挑战性的任务。选择改进的径向基函数神经网络来逼近机器人机械手的不确定动力学并学习不确定性的上限。利用基于李雅普诺夫稳定性理论的自适应律解决径向基函数神经网络权重的统一最终有界问题，保证了闭环系统的稳定性和一致有界跟踪误差。最后，