Identification of dynamic parameters of an industrial robot using a recursively-optimized trajectory

Gu, Yunjin; Wang, Hyuk; Cho, Jae Yong; Lee, Doo Yong

doi:10.1109/iccas.2010.5670148

Cited by 15 publications

(5 citation statements)

References 8 publications

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“…Volume 37 • Number 4 • 2017 • 490 -498 When collecting the torque values, we rotate Joint 1 at uniform speed, which makes both 3 k 2 and 3 k 33 to become zero. At the same time, we rotate Joints 2 and 3 according to a Fourier series excitation trajectory (Gu et al, 2010;Swevers et al, 2007). We constrain the joint angles, the velocities and the accelerations of Joints 1 and 3 to avoid self-collision.…”

Section: Assembly Automationmentioning

confidence: 99%

“…The parameter values from accurate CAD are theoretical results which could be quite different from ground-truth values, as they depend a lot on the manufacturing and assembling processes. Directly using the parameter values obtained from CAD data leads to significant errors in control (Gu et al, 2010). To avoid this problem, engineers use physical experiments to perform dynamic parameter identification.…”

Section: Introductionmentioning

confidence: 99%

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A novel LLSDPso method for nonlinear dynamic parameter identification

Yuan

Wan

et al. 2017

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Purpose This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors). Design/methodology/approach Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively. Findings The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method. Originality/value The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

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Section: Assembly Automationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel LLSDPso method for nonlinear dynamic parameter identification

Yuan

Wan

et al. 2017

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show abstract

“…However, this method has very strict requirement for robot dynamics. About dynamics parameters, there are disassembly measurement method, CAD method and overall identification method [10][11][12]. The most commonly used is parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification and contact interaction control of redundant robot based on dynamic model

Li,

Bian,

Wang

2024

Phys. Scr.

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Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. The least square method is used to obtain dynamic parameters. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

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“…There are generally six distinct types of parameter identification methods for robot link dynamics model. The most common approach is inverse dynamic identification model (IDIM) with ordinary least-squares method (Jin and Gans, 2015; Urrea and Pascal, 2021), and its variants including weighted least-squares (Gu et al , 2010; Liu et al , 2020), total least-squares (Briot and Gautier, 2015) and iteratively reweighted least-squares methods (Han et al , 2020). IDIM with instrumental variables method (Janot et al , 2014a, 2014b; Brunot et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The most common approach is inverse dynamic identification model (IDIM) with ordinary least-squares method (Jin and Gans, 2015; Urrea and Pascal, 2021), and its variants including weighted least-squares (Gu et al , 2010; Liu et al , 2020), total least-squares (Briot and Gautier, 2015) and iteratively reweighted least-squares methods (Han et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

A nonlinear robot joint friction compensation method including stick and sliding characteristics

Feng

Zhang

et al. 2023

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Purpose Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot. Design/methodology/approach This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving. Findings Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods. Originality/value The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

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Identification of dynamic parameters of an industrial robot using a recursively-optimized trajectory

Cited by 15 publications

References 8 publications

A novel LLSDPso method for nonlinear dynamic parameter identification

A novel LLSDPso method for nonlinear dynamic parameter identification

Parameters identification and contact interaction control of redundant robot based on dynamic model

A nonlinear robot joint friction compensation method including stick and sliding characteristics

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