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] Atamturktur, S. [verfasserIn] Platz, R. [verfasserIn] Melz, T. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	friction model Uncertainty quantification inference Parameter calibration

Übergeordnetes Werk:	Enthalten in: Experimental techniques - Cham : Springer International Publishing, 1975, 44(2020), 3 vom: 21. Feb., Seite 369-382
Übergeordnetes Werk:	volume:44 ; year:2020 ; number:3 ; day:21 ; month:02 ; pages:369-382

Links:	Volltext

DOI / URN:	10.1007/s40799-019-00355-7

Katalog-ID:	SPR039874613

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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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allfields	10.1007/s40799-019-00355-7 doi (DE-627)SPR039874613 (SPR)s40799-019-00355-7-e DE-627 ger DE-627 rakwb eng 530 670 690 ASE 52.00 bkl Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213 Atamturktur, S. verfasserin aut Platz, R. verfasserin aut Melz, T. verfasserin aut Enthalten in Experimental techniques Cham : Springer International Publishing, 1975 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)500635854 (DE-600)2205019-X 1747-1567 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://dx.doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 ASE AR 44 2020 3 21 02 369-382
spelling	10.1007/s40799-019-00355-7 doi (DE-627)SPR039874613 (SPR)s40799-019-00355-7-e DE-627 ger DE-627 rakwb eng 530 670 690 ASE 52.00 bkl Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213 Atamturktur, S. verfasserin aut Platz, R. verfasserin aut Melz, T. verfasserin aut Enthalten in Experimental techniques Cham : Springer International Publishing, 1975 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)500635854 (DE-600)2205019-X 1747-1567 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://dx.doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 ASE AR 44 2020 3 21 02 369-382
allfields_unstemmed	10.1007/s40799-019-00355-7 doi (DE-627)SPR039874613 (SPR)s40799-019-00355-7-e DE-627 ger DE-627 rakwb eng 530 670 690 ASE 52.00 bkl Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213 Atamturktur, S. verfasserin aut Platz, R. verfasserin aut Melz, T. verfasserin aut Enthalten in Experimental techniques Cham : Springer International Publishing, 1975 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)500635854 (DE-600)2205019-X 1747-1567 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://dx.doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 ASE AR 44 2020 3 21 02 369-382
allfieldsGer	10.1007/s40799-019-00355-7 doi (DE-627)SPR039874613 (SPR)s40799-019-00355-7-e DE-627 ger DE-627 rakwb eng 530 670 690 ASE 52.00 bkl Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213 Atamturktur, S. verfasserin aut Platz, R. verfasserin aut Melz, T. verfasserin aut Enthalten in Experimental techniques Cham : Springer International Publishing, 1975 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)500635854 (DE-600)2205019-X 1747-1567 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://dx.doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 ASE AR 44 2020 3 21 02 369-382
allfieldsSound	10.1007/s40799-019-00355-7 doi (DE-627)SPR039874613 (SPR)s40799-019-00355-7-e DE-627 ger DE-627 rakwb eng 530 670 690 ASE 52.00 bkl Gehb, C.M. verfasserin aut Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213 Atamturktur, S. verfasserin aut Platz, R. verfasserin aut Melz, T. verfasserin aut Enthalten in Experimental techniques Cham : Springer International Publishing, 1975 44(2020), 3 vom: 21. Feb., Seite 369-382 (DE-627)500635854 (DE-600)2205019-X 1747-1567 nnns volume:44 year:2020 number:3 day:21 month:02 pages:369-382 https://dx.doi.org/10.1007/s40799-019-00355-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 ASE AR 44 2020 3 21 02 369-382
language	English
source	Enthalten in Experimental techniques 44(2020), 3 vom: 21. Feb., Seite 369-382 volume:44 year:2020 number:3 day:21 month:02 pages:369-382
sourceStr	Enthalten in Experimental techniques 44(2020), 3 vom: 21. Feb., Seite 369-382 volume:44 year:2020 number:3 day:21 month:02 pages:369-382
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	friction model Uncertainty quantification inference Parameter calibration
dewey-raw	530
isfreeaccess_bool	false
container_title	Experimental techniques
authorswithroles_txt_mv	Gehb, C.M. @@aut@@ Atamturktur, S. @@aut@@ Platz, R. @@aut@@ Melz, T. @@aut@@
publishDateDaySort_date	2020-02-21T00:00:00Z
hierarchy_top_id	500635854
dewey-sort	3530
id	SPR039874613
language_de	englisch
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author	Gehb, C.M.
spellingShingle	Gehb, C.M. ddc 530 bkl 52.00 misc friction model misc Uncertainty quantification misc inference misc Parameter calibration Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
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topic_title	530 670 690 ASE 52.00 bkl Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model friction model (dpeaa)DE-He213 Uncertainty quantification (dpeaa)DE-He213 inference (dpeaa)DE-He213 Parameter calibration (dpeaa)DE-He213
topic	ddc 530 bkl 52.00 misc friction model misc Uncertainty quantification misc inference misc Parameter calibration
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title	Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
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title_full	Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
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title_sort	bayesian inference based parameter calibration of the lugre-friction model
title_auth	Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
abstract	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.
abstractGer	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.
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.
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title_short	Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model
url	https://dx.doi.org/10.1007/s40799-019-00355-7
remote_bool	true
author2	Atamturktur, S. Platz, R. Melz, T.
author2Str	Atamturktur, S. Platz, R. Melz, T.
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doi_str	10.1007/s40799-019-00355-7
up_date	2024-07-04T01:57:16.568Z
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Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model

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