Framework for Static and Dynamic Friction Identification for Industrial Manipulators

Indri, Marina; Trapani, Stefano

doi:10.1109/tmech.2020.2980435

Cited by 16 publications

(3 citation statements)

References 32 publications

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“…This approach is common in robotics: see e.g. [10], [16]- [18]. When only the link j is moving, while the other links stay at their steady-states, its IDM is given by ( )…”

Section: A First Step: Estimating the Graphical Nature Of Frictionsmentioning

confidence: 99%

A Separable Instrumental Variable Method for Robot Identification

Janot¹

2021

2021 American Control Conference (ACC)

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“…This approach is common in robotics: see e.g. [10], [16]- [18]. When only the link j is moving, while the other links stay at their steady-states, its IDM is given by ( )…”

Section: A First Step: Estimating the Graphical Nature Of Frictionsmentioning

confidence: 99%

A Separable Instrumental Variable Method for Robot Identification

Janot¹

2021

2021 American Control Conference (ACC)

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“…Finally, to check the accuracy and reliability of this method, experimental tests have been performed on a ''walker'' service robot, and the desired results have been presented. Indri and Trapani 15 have presented a general framework for friction identification in industrial mechanical arms with the goal of solving the previous problems. This framework includes a complete management method for all stages, from data acquisition and model identification to code generation for implementation in the robot software, which has made it possible to adopt static or dynamic models with different complexities.…”

Section: Introductionmentioning

confidence: 99%

Extended nonlinear time-varying lugre-based friction model identification of robot manipulator with sliding mode control compensation approach

Moradi

Korayem

Lademakhi

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the values of torque loss due to the friction phenomenon in the robot joints are initially identified; then, by considering the lugre friction model and applying the nonlinear least-squares error-estimation method, a model for this phenomenon is presented. To make the studies more efficient, the general lugre friction model has been used in three forms for modeling the system’s friction that the first model was the general lugre model, and in the other two Extended models, the parameters are considered as time variables that are estimated online. It should be noted that the third model includes the nonlinear viscous term too. Then, by using the nonlinear sliding mode controller, which is a robust control against possible uncertainties in the system, the performance of the designed algorithms is examined. The results show the superiority of the identified models with time-varying parameters over the model with constant parameters. Finally, by the use of represented friction models, a more accurate model for this system has been introduced. Eventually, the results of the simulations have been tested on a 6R laboratory robot to prove the validation of the results with the proposed algorithm, and as it showed, the final friction model has performed better.

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“…The continuity of this function is necessary when using the identified model in the controller for a smooth control signal when the velocity reaches zero. In [6], both static and dynamic friction identification were presented but only for cases where the other dynamic parameters were known. It is possible to convert the static friction model into a linear-in-parameters form.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameter identification method for robotic arms with static friction modelling

Szabó

Szadeczky-Kardoss

2021

ACTA IMEKO

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This paper presents an identification method for robotic manipulators. It demonstrates how a dynamic model can be constructed with the help of the modified Newton–Euler formula. To model the friction of the joints, static friction modelling is used, in which the friction behaviour depends only on the actual velocity of the given joint. With these techniques, the model can be converted into a linear-in-parameters form, which can make the identification process easier. Two estimators are introduced to solve the identification problem, the least-squares and the weighted least-squares estimators, and the determination of the independently identifiable parameter vector to make the regression matrix maximal column rank is presented. The Frobenius norm is used as the condition of the regression matrix to optimise the excitation trajectories, and the form of the trajectories has been selected from the finite Fourier series. The method is tested in a simulated environment to achieve a three-degrees-of-freedom manipulator.

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Framework for Static and Dynamic Friction Identification for Industrial Manipulators

Cited by 16 publications

References 32 publications

A Separable Instrumental Variable Method for Robot Identification

A Separable Instrumental Variable Method for Robot Identification

Extended nonlinear time-varying lugre-based friction model identification of robot manipulator with sliding mode control compensation approach

Dynamic parameter identification method for robotic arms with static friction modelling

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