This paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

本文研究了基于视觉动态（VS）机械手的自适应动态规划（ADP）基于未知动力学的特征跟踪控制方法。基于ADP的最优控制的主要优势在于，将视觉跟踪问题转换为具有最优成本函数的特征跟踪误差控制。此外，开发了一种自适应神经网络观察器来近似整个不确定性，这些不确定性被用于构建改进的成本函数。通过建立批评者神经网络，求解了Hamilton–Jacobi–Bellman（HJB）方程，并推导了近似的最佳误差控制策略。根据李雅普诺夫理论，该闭环VS机械手系统最终被开发的基于ADP的特征跟踪控制策略最终统一约束。最后，