Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method
Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Arora, Rashmi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Shiraz University 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Iranian journal of science and technology - Cham, Switzerland : Springer International Publishing, 1999, 42(2017), 2 vom: 06. Mai, Seite 137-147 |
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| Übergeordnetes Werk: |
volume:42 ; year:2017 ; number:2 ; day:06 ; month:05 ; pages:137-147 |
| Links: |
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| DOI / URN: |
10.1007/s40997-017-0088-y |
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| Katalog-ID: |
SPR038047578 |
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| 520 | |a Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. | ||
| 650 | 4 | |a Physical model reduction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Eigenvalue sensitivity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hybrid manipulator |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bond graph |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Bera, Tarun Kumar |4 aut | |
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10.1007/s40997-017-0088-y doi (DE-627)SPR038047578 (SPR)s40997-017-0088-y-e DE-627 ger DE-627 rakwb eng Arora, Rashmi verfasserin aut Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 Bera, Tarun Kumar aut Enthalten in Iranian journal of science and technology Cham, Switzerland : Springer International Publishing, 1999 42(2017), 2 vom: 06. Mai, Seite 137-147 (DE-627)844238007 (DE-600)2843074-8 2364-1835 nnns volume:42 year:2017 number:2 day:06 month:05 pages:137-147 https://dx.doi.org/10.1007/s40997-017-0088-y 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2017 2 06 05 137-147 |
| spelling |
10.1007/s40997-017-0088-y doi (DE-627)SPR038047578 (SPR)s40997-017-0088-y-e DE-627 ger DE-627 rakwb eng Arora, Rashmi verfasserin aut Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 Bera, Tarun Kumar aut Enthalten in Iranian journal of science and technology Cham, Switzerland : Springer International Publishing, 1999 42(2017), 2 vom: 06. Mai, Seite 137-147 (DE-627)844238007 (DE-600)2843074-8 2364-1835 nnns volume:42 year:2017 number:2 day:06 month:05 pages:137-147 https://dx.doi.org/10.1007/s40997-017-0088-y 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2017 2 06 05 137-147 |
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10.1007/s40997-017-0088-y doi (DE-627)SPR038047578 (SPR)s40997-017-0088-y-e DE-627 ger DE-627 rakwb eng Arora, Rashmi verfasserin aut Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 Bera, Tarun Kumar aut Enthalten in Iranian journal of science and technology Cham, Switzerland : Springer International Publishing, 1999 42(2017), 2 vom: 06. Mai, Seite 137-147 (DE-627)844238007 (DE-600)2843074-8 2364-1835 nnns volume:42 year:2017 number:2 day:06 month:05 pages:137-147 https://dx.doi.org/10.1007/s40997-017-0088-y 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2017 2 06 05 137-147 |
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10.1007/s40997-017-0088-y doi (DE-627)SPR038047578 (SPR)s40997-017-0088-y-e DE-627 ger DE-627 rakwb eng Arora, Rashmi verfasserin aut Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 Bera, Tarun Kumar aut Enthalten in Iranian journal of science and technology Cham, Switzerland : Springer International Publishing, 1999 42(2017), 2 vom: 06. Mai, Seite 137-147 (DE-627)844238007 (DE-600)2843074-8 2364-1835 nnns volume:42 year:2017 number:2 day:06 month:05 pages:137-147 https://dx.doi.org/10.1007/s40997-017-0088-y 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2017 2 06 05 137-147 |
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10.1007/s40997-017-0088-y doi (DE-627)SPR038047578 (SPR)s40997-017-0088-y-e DE-627 ger DE-627 rakwb eng Arora, Rashmi verfasserin aut Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 Bera, Tarun Kumar aut Enthalten in Iranian journal of science and technology Cham, Switzerland : Springer International Publishing, 1999 42(2017), 2 vom: 06. Mai, Seite 137-147 (DE-627)844238007 (DE-600)2843074-8 2364-1835 nnns volume:42 year:2017 number:2 day:06 month:05 pages:137-147 https://dx.doi.org/10.1007/s40997-017-0088-y 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2017 2 06 05 137-147 |
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Arora, Rashmi |
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Arora, Rashmi misc Physical model reduction misc Eigenvalue sensitivity misc Hybrid manipulator misc Bond graph Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method |
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Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method Physical model reduction (dpeaa)DE-He213 Eigenvalue sensitivity (dpeaa)DE-He213 Hybrid manipulator (dpeaa)DE-He213 Bond graph (dpeaa)DE-He213 |
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physical model reduction of a parallel and hybrid manipulator using eigenvalue sensitivity method |
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Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method |
| abstract |
Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. © Shiraz University 2017 |
| abstractGer |
Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. © Shiraz University 2017 |
| abstract_unstemmed |
Abstract The multi-energy domain dynamic systems are of utmost importance; a number of researchers have carried out their modelling by taking advantage of this concept. The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. The comparisons of results for full and reduced models are also given in the form of absolute and percentage errors. © Shiraz University 2017 |
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Physical Model Reduction of a Parallel and Hybrid Manipulator Using Eigenvalue Sensitivity Method |
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10.1007/s40997-017-0088-y |
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2024-07-03T15:54:46.082Z |
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The dynamic systems are very complex in nature as the time required for simulation is large and their computations become difficult. Therefore, the model reduction is done such that the dynamics of the systems are preserved and accuracy is maintained. With this, the simulation time and number of calculations are reduced considerably. The contribution of this paper is that modelling of a parallel and hybrid manipulator is done using bond graph modelling technique, and then, their dynamic models are reduced with the eigenvalue sensitivity method for model reduction. This paper attempts to achieve this by mathematically finding the redundant elements, which do not add to the dynamics of the system but increase the calculation time by increasing the complexity of the system. The redundant elements could be neglected, and hence, the complexity reduced without compromising the accuracy. The eigenvalues are calculated from the system matrix, and effect matrices are formed. The row and column based on eigenvalues can be reduced to form a reduced model. The trajectory tracking is done for both the manipulators, and results are presented in this paper for full and reduced models. 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