Decoupled elastostatic stiffness modeling of parallel manipulators based on the rigidity principle
Decoupled elastostatic stiffness modeling of parallel manipulators based on the rigidity principle
复制标题
基于刚度原理的并联机构解耦弹静刚度建模
DOI：
10.1016/j.mechmachtheory.2019.103718
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发表时间：
2020-03
影响因子：
5.2
通讯作者：
Chen Qiaohong
中科院分区：
文献类型：
--
作者：
Yang Chao;Li Qinchuan;Chen Qiaohong
基于刚度原理、旋量理论和应变能，考虑连杆、关节和作动器的柔性，建立了并联机器人的弹性静力刚度解析模型。该模型可以解耦各弹性元件对机构刚度性能的贡献，适用于具有亚闭环结构的非过约束和过约束永磁机构。该方法的实现是：1）基于螺旋理论建立肢体约束扳手的数学模型，2）将机构中的弹性构件依次设为柔性构件，其余构件设为刚性构件，计算机构中各弹性构件的柔度矩阵贡献（CMC）和弹性挠度贡献（EDC）;以及3）通过对这些CMC和EDC求和来获得机构的总偏转和总柔度矩阵。以平行四边形并联机器人（PTPM）和3 PRRR并联机器人为例进行了仿真，结果表明，选择性地提高弹性元件的刚度性能可以最有效地提高机构的线/角刚度性能。该模型为提高机构的线刚度/角刚度性能提供了一种有效的途径。
Analytical elastostatic stiffness modeling of parallel manipulators (PMs) based on the rigidity principle, screw theory, and strain energy while considering the flexibility of the links, joints, and actuators is proposed. The proposed model can decouple each elastic component's contribution to the mechanism's stiffness performance and is suitable for application to non-overconstrained and overconstrained PMs with sub-closed loop structures. The method is implemented as follows: 1) formulate limb constraint wrenches based on screw theory; 2) let each elastic component be flexible sequentially while the remainder are rigid and calculate the compliance matrix contribution (CMC) and the elastic deflection contribution (EDC) for each elastic component in the mechanism; and 3) obtain the total deflection and overall compliance matrix of the mechanism by summing these CMCs and EDCs. The parallelogram-type parallel manipulator (PTPM) and 3PRRR PM are used as illustrative examples to implement the proposed model and the results show that selective improvement of the stiffness performances of the elastic components can improve the linear/angular stiffness performance of the mechanism most effectively. The proposed model provides an effective approach to improve the linear/angular stiffness performances of mechanisms.
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重载并联机构的近似刚度建模与刚度缺陷识别
影响因子：
2.7
作者：
Fan, Shuai;Fan, Shouwen
通讯作者：
Fan, Shouwen
DOI：
10.1109/robot.2001.933124
发表时间：
2001-05
期刊：
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)
影响因子：
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作者：
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通讯作者：
Tian Huang;Xingyu Zhao;D. Whitehouse
影响因子：
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作者：
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通讯作者：
Charles Pinto;J. Corral;O. Altuzarra;A. Hernández
影响因子：
2.3
作者：
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通讯作者：
Guang Yu;Jun Wu;Liping Wang
影响因子：
2.1
作者：
Zhen Huang;Y. Zhao;Jingfang Liu
通讯作者：
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