Identification of the parameters of robot manipulators dynamics about an operating point using perturbed dynamics

Kinsheel, Azeddien; Taha, Zahari

doi:10.1109/icarcv.2010.5707426

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…( , , )    τ Y q q q θ (9) to coincide with the above notation in section 3.1. The prediction error for the dynamic regression model can be written as…”

Section: Dynamic and Filtered-dynamic Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The identification trajectories usually consist of one or two joints motions at a time, which can be generated by simple controllers. Examples of such identification methods can be found in [7,8,9]. On the other hand, direct identification methods are based on a single motion trajectory that excites all the parameters of the robot dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Chávez-Olivares

Reyes-Cortés

González-Galván

et al. 2012

International Journal of Advanced Robotic Systems

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The inertial and friction parameters of a robot are used in the development and evaluation of modelbased control schemes and their accuracy is related directly to the performance. These parameters can also be used for a realistic simulation, which may be useful before implementation of new control schemes. In principle, the numerical value of the parameters could be obtained via CAD analysis, but inevitably assembly and manufacturing errors exist. Direct measurement is not a realistic option because the complex nature of the system involves intense, time-consuming effort. Alternatively, we can deduce the values of the parameters by observing the natural response of the system under appropriate experimental conditions, i.e., by using identification schemes, which is an efficient way. This paper presents the experimental evaluation of five identification schemes used to obtain the inertial and friction parameters of a three-degrees-of-freedom direct-drive robot. We assume that the inertial and friction parameters are totally unknown but, by design, the dynamic model is fully known, as in many practical cases. We consider the schemes based on the dynamic regression model, the filtered-dynamic regression model, the supplied-energy regression model, the power regression model and the filtered-power regression model. This paper presents a comparison between experimental and simulated robot response, which enables us to verify the accuracy of each regression model.

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“…( , , )    τ Y q q q θ (9) to coincide with the above notation in section 3.1. The prediction error for the dynamic regression model can be written as…”

Section: Dynamic and Filtered-dynamic Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Chávez-Olivares

Reyes-Cortés

González-Galván

et al. 2012

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Examples of such identification methods can be found in Benimeli et al, 11 Grotjahn et al, 12 and Kinsheel and Taha. 13 On the other hand, the direct identification methods are based on a single-motion trajectory that excites all the parameters of the robot dynamics. The challenging part of this method is the design of the identification trajectory that leads to an optimum estimation of the system parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of such identification methods can be found in Benimeli et al, 11 Grotjahn et al, 12 and Kinsheel and Taha. 13…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of parameter identification schemes on a direct-drive robot

Chávez-Olivares

Reyes-Cortés

González-Galván

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part I:

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The dynamic and friction parameters of a robot are used in advanced control schemes, and their accuracy significantly affects their performance. These parameters can also be used for a realistic simulation. In principle, the numerical value of the parameters could be obtained via computer-aided design analysis but inevitable assembly and manufacturing errors exist. Direct measurement is not a realistic option because the complex nature of the system would involve an intense time-consuming effort. Alternatively, we can deduce the values of the parameters by observing the natural response of the system under appropriate experimental conditions, that is, by using identification schemes. This article presents the experimental evaluation of five identification schemes used to obtain the dynamic and friction parameters of a two-degree-of-freedom, direct-drive robot. We assume that the dynamic and friction parameters are totally unknown but, by design, the dynamic model is fully known. We consider the schemes based on the dynamic regression model, filtered-dynamic regression model, supplied-energy regression model, power regression model, and filtered-power regression model. The article presents a comparison between experimental and simulated robot responses, which enable us to verify the accuracy of each regression model.

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Parameter Estimation for Multibody System Dynamic Model of Delta Robot From Experimental Data

et al. 2021

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Identification of the parameters of robot manipulators dynamics about an operating point using perturbed dynamics

Cited by 6 publications

References 11 publications

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

Experimental evaluation of parameter identification schemes on a direct-drive robot

Parameter Estimation for Multibody System Dynamic Model of Delta Robot From Experimental Data

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