Modeling and Self-Learning Soft-Grasp Control for Free-Floating Space Manipulator During Target Capturing Using Variable Stiffness Method

被引:22
作者
Chu, Ming [1 ]
Wu, Xingyu [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China
基金
中国国家自然科学基金;
关键词
Coupling angular momentum; post-impact control; variable stiffness; cellular differential evolution; adaptive backstepping; AUTONOMOUS ROBOTIC CAPTURE; CONFIGURATION; DYNAMICS; SYSTEMS;
D O I
10.1109/ACCESS.2017.2788400
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
During target capturing operation, the changes in the dynamics parameters of a free-floating space manipulator degrade the performance of the base attitude stabilization. This paper presents a new self-learning soft-grasp control algorithm based on the variable stiffness technology. First, the dynamic model of variable stiffness joint space manipulator system is established. Simultaneously, the detailed dynamic analysis of pre-impact and post-impact stages is carried out. Second, a new soft-grasp control strategy utilizing cellular differential evolution algorithm combined opposition-based learning with orthogonal crossover is employed to minimize the base angular momentum. Its principle is to solve the optimal stiffness value of the variable stiffness joint to realize desired buffering. Thereafter, we put forward an adaptive backstepping sliding mode control method to track the actual joint stiffness. Finally, the proposed method is applied to a two-degree of freedom planar free-floating space manipulator and the simulation results verify the effectiveness.
引用
收藏
页码:7044 / 7054
页数:11
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