Optimal robust online tracking control for space manipulator in task space using off-policy reinforcement learning

被引：0

作者：

Zhuang, Hongji ^{[1
]}

Zhou, Hang ^{[1
]}

Shen, Qiang ^{[1
]}

Wu, Shufan ^{[1
,2
]}

Razoumny, Vladimir Yu. ^{[2
]}

Razoumny, Yury N. ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China

[2] Peoples Friendship Univ Russia, RUDN Univ, Moscow 117198, Russia

来源：

AEROSPACE SCIENCE AND TECHNOLOGY | 2024年 / 153卷

基金：

中国国家自然科学基金;

关键词：

Task space; Reinforcement learning; Online tracking control; H-INFINITY CONTROL; ROBOT MANIPULATORS; NONLINEAR-SYSTEMS; TIME-SYSTEMS; APPROXIMATION; STATE;

D O I：

10.1016/j.ast.2024.109446

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This study addresses the demands for adaptability, uncertainty management, and high performance in the control of space manipulators, and the inadequacies in achieving optimal control and handling external uncertainty in task space in previous research. Based on off-policy reinforcement learning, a model-free and time-efficient method for online robust tracking control in task space is devised. To address the complexity of dynamic equations in task space, a mixed-variable approach is adopted to transform the multivariable coupled time- varying problem into a single-variable problem. Subsequently, the optimal control policy is derived with the disturbance convergence, stability, and optimality of the control method being demonstrated. This marks the first instance of achieving robust optimal tracking control in task space for space manipulators. The efficacy and superiority of the presented algorithm are validated through simulation.

引用

页数：12

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