Experimental parameter identification of flexible joint robot manipulators

Miranda-Colorado, Roger; Moreno–Valenzuela, Javier

doi:10.1017/s0263574717000224

Cited by 41 publications

(37 citation statements)

References 44 publications

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“…But it can be found that t e is linear with F sm . Thus, by substituting equations (18) and (19) into equation 1, it can be obtained as follows…”

Section: Identification Of the Motion-dependent Parametersmentioning

confidence: 99%

A dynamic parameter identification method of industrial robots considering joint elasticity

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

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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 motiondependent 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.

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“…But it can be found that t e is linear with F sm . Thus, by substituting equations (18) and (19) into equation 1, it can be obtained as follows…”

Section: Identification Of the Motion-dependent Parametersmentioning

confidence: 99%

A dynamic parameter identification method of industrial robots considering joint elasticity

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…In the experimental platform, the motor torque constant matrix K m and servo amplifier gain matrix K sa have the values Regarding the dynamic model equations (1) and (2), the corresponding matrices for the experimental platform are 59,60…”

Section: Platform Descriptionmentioning

confidence: 99%

“…A detailed procedure description of this parameter identification methodology is given in the literature. 60 The numerical values of the estimated parameters for the 2-DOF FJR manipulator are shown in Table 1.…”

Section: F C3mentioning

confidence: 99%

A model-based velocity controller for chaotization of flexible joint robot manipulators

Miranda-Colorado

Aguilar

Moreno–Valenzuela

2018

International Journal of Advanced Robotic Systems

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This article presents a model-based velocity controller able to induce a chaotic motion on n-degrees of freedom flexible joint robot manipulators. The proposed controller allows the velocity link vector of a robot manipulator to track an arbitrary, chaotic reference vector field. A rigorous theoretical analysis based on Lyapunov's theory is used to prove the asymptotic stability of the tracking error signals when using the proposed controller, which implies that a chaotic motion is induced to the robotic system. Experimental results are provided using a flexible joint robot manipulator of two degrees of freedom. Finally, by using Poincaré maps and Lyapunov exponents, it is shown that the behavior exhibited by the robot joint positions is chaotic.

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“…The aim of this paper is therefore to evaluate the robustness of output error methods for robot identification which has attracted some renewed interest over the past few years [15][16][17][18][19][20] among others. In pursuing this goal, this work shows that it is more interesting to consider the input signal rather than the output one due to the larger sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Output Error Methods for Robot Identification

Brunot

Janot

Carrillo

et al. 2019

Journal of Dynamic Systems, Measurement, and Control

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Industrial robot identification is usually based on the inverse dynamic identification model (IDIM) that comes from Newton's laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the least-squares (LS) or the instrumental variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model's parameters are iteratively tuned in order to match the simulated model's output and the measured system's output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a six degrees-of-freedom rigid robot.

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Experimental parameter identification of flexible joint robot manipulators

Cited by 41 publications

References 44 publications

A dynamic parameter identification method of industrial robots considering joint elasticity

A dynamic parameter identification method of industrial robots considering joint elasticity

A model-based velocity controller for chaotization of flexible joint robot manipulators

Output Error Methods for Robot Identification

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