Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functi...
Ausführliche Beschreibung
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| Autor*in: |
Gehb, C.M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society for Experimental Mechanics, Inc 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Experimental techniques - Springer International Publishing, 1980, 44(2020), 3 vom: 21. Feb., Seite 369-382 |
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| Übergeordnetes Werk: |
volume:44 ; year:2020 ; number:3 ; day:21 ; month:02 ; pages:369-382 |
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| DOI / URN: |
10.1007/s40799-019-00355-7 |
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| 520 | |a Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. | ||
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10.1007/s40799-019-00355-7 doi (DE-627)OLC2062420617 (DE-He213)s40799-019-00355-7-p DE-627 ger DE-627 rakwb eng 530 670 690 VZ Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Society for Experimental Mechanics, Inc 2020 Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. friction model Uncertainty quantification inference Parameter calibration Atamturktur, S. aut Platz, R. aut Melz, T. aut Enthalten in Experimental techniques Springer International Publishing, 1980 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)130750042 (DE-600)990656-3 (DE-576)016298004 0732-8818 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2014 GBV_ILN_2048 AR 44 2020 3 21 02 369-382 |
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10.1007/s40799-019-00355-7 doi (DE-627)OLC2062420617 (DE-He213)s40799-019-00355-7-p DE-627 ger DE-627 rakwb eng 530 670 690 VZ Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Society for Experimental Mechanics, Inc 2020 Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. friction model Uncertainty quantification inference Parameter calibration Atamturktur, S. aut Platz, R. aut Melz, T. aut Enthalten in Experimental techniques Springer International Publishing, 1980 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)130750042 (DE-600)990656-3 (DE-576)016298004 0732-8818 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2014 GBV_ILN_2048 AR 44 2020 3 21 02 369-382 |
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10.1007/s40799-019-00355-7 doi (DE-627)OLC2062420617 (DE-He213)s40799-019-00355-7-p DE-627 ger DE-627 rakwb eng 530 670 690 VZ Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Society for Experimental Mechanics, Inc 2020 Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. friction model Uncertainty quantification inference Parameter calibration Atamturktur, S. aut Platz, R. aut Melz, T. aut Enthalten in Experimental techniques Springer International Publishing, 1980 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)130750042 (DE-600)990656-3 (DE-576)016298004 0732-8818 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2014 GBV_ILN_2048 AR 44 2020 3 21 02 369-382 |
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10.1007/s40799-019-00355-7 doi (DE-627)OLC2062420617 (DE-He213)s40799-019-00355-7-p DE-627 ger DE-627 rakwb eng 530 670 690 VZ Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Society for Experimental Mechanics, Inc 2020 Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. friction model Uncertainty quantification inference Parameter calibration Atamturktur, S. aut Platz, R. aut Melz, T. aut Enthalten in Experimental techniques Springer International Publishing, 1980 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)130750042 (DE-600)990656-3 (DE-576)016298004 0732-8818 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2014 GBV_ILN_2048 AR 44 2020 3 21 02 369-382 |
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10.1007/s40799-019-00355-7 doi (DE-627)OLC2062420617 (DE-He213)s40799-019-00355-7-p DE-627 ger DE-627 rakwb eng 530 670 690 VZ Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Society for Experimental Mechanics, Inc 2020 Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. friction model Uncertainty quantification inference Parameter calibration Atamturktur, S. aut Platz, R. aut Melz, T. aut Enthalten in Experimental techniques Springer International Publishing, 1980 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)130750042 (DE-600)990656-3 (DE-576)016298004 0732-8818 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2014 GBV_ILN_2048 AR 44 2020 3 21 02 369-382 |
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Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. © The Society for Experimental Mechanics, Inc 2020 |
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Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. © The Society for Experimental Mechanics, Inc 2020 |
| abstract_unstemmed |
Abstract Load redistribution in smart load bearing mechanical structures can be used to reduce negative effects of damage or to prevent further damage if predefined load paths become unsuitable. Using controlled friction brakes in joints of kinematic links can be a suitable way to add dynamic functionality for desired load path redistribution. Therefore, adequate friction models are needed to predict the friction behavior. Possible models that can be used to model friction vary from simple static to complex dynamic models with increasing sophistication in the representation of friction phenomena. The LuGre-model is a widely used dynamic friction model for friction compensation in high precision control systems. It needs six parameters for describing the friction behavior. These parameters are coupled to an unmeasurable internal state variable, therefore, parameter identification is challenging. Conventionally, optimization algorithms are used to identify the LuGre-parameters deterministically. In this paper, the parameter identification and calibration is formulated to achieve model prediction that is statistically consistent with the experimental data. By use of the $ R^{2} $ sensitivity analysis, the most influential parameters are selected for calibration. Subsequently, the Bayesian inference based calibration procedure using experimental data is performed. Uncertainty represented in former wide parameter ranges can be reduced and, thus, model prediction accuracy can be increased. © The Society for Experimental Mechanics, Inc 2020 |
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Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model |
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Atamturktur, S. Platz, R. Melz, T. |
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