Identification of Dynamic Parameters for Robots with Elastic Joints

Zollo, Loredana; Lopez, Edoardo; Spedaliere, Luca; García-Aracil, Nicolás; Guglielmelli, Eugenio

doi:10.1155/2014/843186

Cited by 16 publications

(14 citation statements)

References 24 publications

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“…Table III shows the nominal values provided by the robot manufacturer, and the corresponding estimated values obtained using Eqs. (28) and (30). These estimated values correspond to those obtained at 10[s] after starting the parameter identification experiment.…”

Section: Experimental Parameter Identificationsupporting

confidence: 71%

“…The values δ = 1 and P (0) = 10I 15 , with I 15 being the identity matrix in IR 15×15 , were used in the parameter identification experiments using the LSFF method in Eq. (30). For the proposed off-line identification procedure and for the LSFF identification method in Eq.…”

Section: Experimental Parameter Identificationmentioning

confidence: 99%

“…Finally, more complex identification procedures for parameter identification of FJR manipulators were given in refs. [29] and [30]. Specifically, in the work, 29 the elements of the gravity vector and the inertia matrix for the KUKA LWR were identified.…”

Section: Introductionmentioning

confidence: 99%

“…The work in ref. [30] reported a regressor-based methodology that allows estimating all the dynamic parameters, but only simulations results were provided.…”

Section: Introductionmentioning

confidence: 99%

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Experimental parameter identification of flexible joint robot manipulators

Miranda-Colorado¹,

Moreno–Valenzuela²

2017

Robotica

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SUMMARYThis paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

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Section: Experimental Parameter Identificationsupporting

confidence: 71%

Section: Experimental Parameter Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The work in ref. [30] reported a regressor-based methodology that allows estimating all the dynamic parameters, but only simulations results were provided.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental parameter identification of flexible joint robot manipulators

Miranda-Colorado¹,

Moreno–Valenzuela²

2017

Robotica

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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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