所属单位：电气与电子工程学院
发表刊物：Complex & Intelligent Systems
项目来源：国家自然科学基金项目
关键字：Adaptive dynamic programming · Neural networks · Optimal control · Visual servoing · Unknown dynamics
摘要：This paper investigates a feature tracking controlmethod 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.
合写作者：李洪文,任晓琳
第一作者：任晓琳
论文类型：期刊论文
页面范围：1
ISSN号：2199-4536
是否译文：否
发表时间：2021-04-20