Optimal Sliding Mode Tracking Control for Reconfigurable Manipulators Based on Adaptive Dynamic Programming

被引：0
|
作者：
Ma, Bing [1 ]
Dong, Bo [1 ]
Zhou, Fan [1 ]
Li, Ruiyang [1 ]
Yu, Miao [1 ,2 ]
Li, Yuanchun [1 ]
机构：
[1] Changchun Univ Technol, Dept Control Sci & Engn, Changchun, Jilin, Peoples R China
[2] CRRC Changchun Railway Vehicles CO LTD, Changchun, Jilin, Peoples R China
来源：
2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年
基金：
中国国家自然科学基金;
关键词：
reconfigurable manipulators; adaptive dynamic programming; critic neural network; sliding mode control; optimal control; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel optimal sliding mode tracking control method for reconfigurable manipulators based on the policy iteration (PI) and adaptive dynamic programming (ADP). The designed sliding mode tracking control can suppress the tracking error caused by reconfiguration of the manipulators. Based on ADP and PI algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic neural network and then the approximated optimal sliding mode control policy can be derived directly. Based on the Lyapunov stability theorem, the closed-loop robotic system is proved to be asymptotic stability. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.
引用
收藏
页码：952 / 958
页数：7
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