A multi-objective fusion internal preload optimization method of redundantly actuated parallel robots for backlash prevention

被引：0
|
作者：
Su, Tingting [1 ]
Liang, Xu [1 ,2 ]
Liang, Jiyu [1 ]
Wang, Weiqun [2 ,3 ]
He, Guangping [1 ]
Zhao, Quanliang [1 ]
Zhao, Lei [1 ]
Huang, Can [1 ]
Jiao, Yuze [2 ]
机构：
[1] North China Univ Technol, Dept Mech & Elect Engn, Beijing 100144, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[3] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
来源：
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2021年
关键词：
DELTA ROBOT; DYNAMICS;
D O I：
10.1109/IJCNN52387.2021.9534277
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a multi-objective internal preload optimization method for a 4RRR redundantly actuated parallel robot is proposed. First, the dynamic model of a redundantly actuated parallel robot with redundancy of 2 is studied. The inverse dynamics model of the redundantly actuated parallel robot has an infinite set of control torque solutions that can track the desired trajectory, which makes the redundantly actuated parallel manipulators have the ability to complete additional tasks during operation. Taking the elimination of transmission clearance as an additional control task, the multi-objective internal preload optimization method is proposed, which takes the driving power, the switching loss of the drivers and the control force as the optimization goals. By utilizing this method, the backlash or clearance in the system can be eliminated, the power loss of the drivers can be reduced, and the operation precision can be improved. Some numerical simulations demonstrate the feasibility of the proposed method, and the robot shows stable motion characteristics.
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页数：6
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