scholarly journals A dynamic parameter identification method of industrial robots considering joint elasticity

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882521 ◽  
Author(s):  
Hepeng Ni ◽  
Chengrui Zhang ◽  
Tianliang Hu ◽  
Teng Wang ◽  
Qizhi Chen ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Joint Stiffness ◽  
Dynamic Parameter ◽  
Industrial Robots ◽  
Identification Accuracy ◽  
Processing Algorithm ◽  
Identification Method ◽  
Joint Elasticity ◽  
Dynamic Parameter Identification

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motion-dependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

scholarly journals Dynamic Parameter Identification of a Pointing Mechanism Considering the Joint Clearance

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Jing Sun ◽  
Xueyan Han ◽  
Tong Li ◽  
Shihua Li
Keyword(s):  
Parameter Identification ◽  
Dynamic Parameter ◽  
Identification Accuracy ◽  
Joint Clearance ◽  
Nonlinear Dynamic Model ◽  
Revolute Joint ◽  
Bee Colony ◽  
Adams Software ◽  
Observation Matrix ◽  
Dynamic Parameter Identification

The clearance of the revolute joint influences the accuracy of dynamic parameter identification. In order to address this problem, a method for dynamic parameter identification of an X–Y pointing mechanism while considering the clearance of the revolute joint is proposed in this paper. Firstly, the nonlinear dynamic model of the pointing mechanism was established based on a modified contact model, which took the effect of the asperity of contact surface on joint clearance into consideration. Secondly, with the aim of achieving the anti-interference incentive trajectory, the trajectory was optimized according to the condition number of the observation matrix and the driving functions of activate joints that could be obtained. Thirdly, dynamic simulation was conducted through Adams software, and clearance was involved in the simulation model. Finally, the dynamic parameter identification of the pointing mechanism was conducted based on an artificial bee colony (ABC) algorithm. The identification result that considered joint clearance was compared with that which did not consider joint clearance. The results showed that the accuracy of the dynamic parameter identification was improved when the clearance was taken into consideration. This study provides a theoretical basis for the improvement of dynamic parameter identification accuracy.

scholarly journals Closed-Loop Dynamic Parameter Identification of Robot Manipulators Using Modified Fourier Series

10.5772/45818 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Wenxiang Wu ◽  
Shiqiang Zhu ◽  
Xuanyin Wang ◽  
Huashan Liu
Keyword(s):  
Fourier Series ◽  
Parameter Identification ◽  
Closed Loop ◽  
Robot Manipulators ◽  
Dynamic Parameter ◽  
Measurement Noise ◽  
Identification Accuracy ◽  
Parameter Estimates ◽  
Identification Procedure ◽  
Dynamic Parameter Identification

This paper concerns the problem of dynamic parameter identification of robot manipulators and proposes a closed-loop identification procedure using modified Fourier series (MFS) as exciting trajectories. First, a static continuous friction model is involved to model joint friction for realizable friction compensation in controller design. Second, MFS satisfying the boundary conditions are firstly designed as periodic exciting trajectories. To minimize the sensitivity to measurement noise, the coefficients of MFS are optimized according to the condition number criterion. Moreover, to obtain accurate parameter estimates, the maximum likelihood estimation (MLE) method considering the influence of measurement noise is adopted. The proposed identification procedure has been implemented on the first three axes of the QIANJIANG-I 6-DOF robot manipulator. Experiment results verify the effectiveness of the proposed approach, and comparison between identification using MFS and that using finite Fourier series (FFS) reveals that the proposed method achieves better identification accuracy.

scholarly journals Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

2019 ◽  
Vol 9 (2) ◽  
pp. 324 ◽  
Author(s):  
Fusheng Zha ◽  
Wentao Sheng ◽  
Wei Guo ◽  
Shiyin Qiu ◽  
Jing Deng ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Parameter Identification ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Experimental Conditions ◽  
Dynamic Parameter Identification

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

scholarly journals A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

2021 ◽  
Vol 11 (22) ◽  
pp. 10988
Author(s):  
Jun Cheng ◽  
Shusheng Bi ◽  
Chang Yuan ◽  
Lin Chen ◽  
Yueri Cai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Theoretical Basis ◽  
Dynamic Parameter ◽  
Dynamic Parameters ◽  
Precision Manufacturing ◽  
Robot System ◽  
Precise Control ◽  
Dynamic Parameter Identification

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

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