Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables

Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontin...
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Gespeichert in:

Autor*in:	Peng, Haijun [verfasserIn] Zhang, Mengru

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multibody system dynamics Discrete variables Continuous method Dynamic optimization Fully coupled optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multibody system dynamics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997, 60(2023), 3 vom: 01. Juni, Seite 475-496
Übergeordnetes Werk:	volume:60 ; year:2023 ; number:3 ; day:01 ; month:06 ; pages:475-496

Links:	Volltext

DOI / URN:	10.1007/s11044-023-09918-4

Katalog-ID:	SPR054930839

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520			\|a Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables.
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allfields	10.1007/s11044-023-09918-4 doi (DE-627)SPR054930839 (SPR)s11044-023-09918-4-e DE-627 ger DE-627 rakwb eng Peng, Haijun verfasserin aut Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213 Zhang, Mengru aut Enthalten in Multibody system dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 60(2023), 3 vom: 01. Juni, Seite 475-496 (DE-627)271181087 (DE-600)1479537-1 1573-272X nnns volume:60 year:2023 number:3 day:01 month:06 pages:475-496 https://dx.doi.org/10.1007/s11044-023-09918-4 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_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_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_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 AR 60 2023 3 01 06 475-496
spelling	10.1007/s11044-023-09918-4 doi (DE-627)SPR054930839 (SPR)s11044-023-09918-4-e DE-627 ger DE-627 rakwb eng Peng, Haijun verfasserin aut Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213 Zhang, Mengru aut Enthalten in Multibody system dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 60(2023), 3 vom: 01. Juni, Seite 475-496 (DE-627)271181087 (DE-600)1479537-1 1573-272X nnns volume:60 year:2023 number:3 day:01 month:06 pages:475-496 https://dx.doi.org/10.1007/s11044-023-09918-4 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_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_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_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 AR 60 2023 3 01 06 475-496
allfields_unstemmed	10.1007/s11044-023-09918-4 doi (DE-627)SPR054930839 (SPR)s11044-023-09918-4-e DE-627 ger DE-627 rakwb eng Peng, Haijun verfasserin aut Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213 Zhang, Mengru aut Enthalten in Multibody system dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 60(2023), 3 vom: 01. Juni, Seite 475-496 (DE-627)271181087 (DE-600)1479537-1 1573-272X nnns volume:60 year:2023 number:3 day:01 month:06 pages:475-496 https://dx.doi.org/10.1007/s11044-023-09918-4 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_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_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_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 AR 60 2023 3 01 06 475-496
allfieldsGer	10.1007/s11044-023-09918-4 doi (DE-627)SPR054930839 (SPR)s11044-023-09918-4-e DE-627 ger DE-627 rakwb eng Peng, Haijun verfasserin aut Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213 Zhang, Mengru aut Enthalten in Multibody system dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 60(2023), 3 vom: 01. Juni, Seite 475-496 (DE-627)271181087 (DE-600)1479537-1 1573-272X nnns volume:60 year:2023 number:3 day:01 month:06 pages:475-496 https://dx.doi.org/10.1007/s11044-023-09918-4 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_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_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_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 AR 60 2023 3 01 06 475-496
allfieldsSound	10.1007/s11044-023-09918-4 doi (DE-627)SPR054930839 (SPR)s11044-023-09918-4-e DE-627 ger DE-627 rakwb eng Peng, Haijun verfasserin aut Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213 Zhang, Mengru aut Enthalten in Multibody system dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 60(2023), 3 vom: 01. Juni, Seite 475-496 (DE-627)271181087 (DE-600)1479537-1 1573-272X nnns volume:60 year:2023 number:3 day:01 month:06 pages:475-496 https://dx.doi.org/10.1007/s11044-023-09918-4 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_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_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_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 AR 60 2023 3 01 06 475-496
language	English
source	Enthalten in Multibody system dynamics 60(2023), 3 vom: 01. Juni, Seite 475-496 volume:60 year:2023 number:3 day:01 month:06 pages:475-496
sourceStr	Enthalten in Multibody system dynamics 60(2023), 3 vom: 01. Juni, Seite 475-496 volume:60 year:2023 number:3 day:01 month:06 pages:475-496
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multibody system dynamics Discrete variables Continuous method Dynamic optimization Fully coupled optimization
isfreeaccess_bool	false
container_title	Multibody system dynamics
authorswithroles_txt_mv	Peng, Haijun @@aut@@ Zhang, Mengru @@aut@@
publishDateDaySort_date	2023-06-01T00:00:00Z
hierarchy_top_id	271181087
id	SPR054930839
language_de	englisch
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author	Peng, Haijun
spellingShingle	Peng, Haijun misc Multibody system dynamics misc Discrete variables misc Continuous method misc Dynamic optimization misc Fully coupled optimization Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
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topic_title	Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables Multibody system dynamics (dpeaa)DE-He213 Discrete variables (dpeaa)DE-He213 Continuous method (dpeaa)DE-He213 Dynamic optimization (dpeaa)DE-He213 Fully coupled optimization (dpeaa)DE-He213
topic	misc Multibody system dynamics misc Discrete variables misc Continuous method misc Dynamic optimization misc Fully coupled optimization
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title	Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
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title_full	Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
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title_sort	continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
title_auth	Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
abstract	Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Considering the manufacturing process and component specifications in engineering, it is of great significance to investigate the optimization problem with discrete design variables. However, the discreteness of the feasible set of discrete variables will result in a nonconvex and discontinuous optimization problem. It renders traditional continuous variable optimization methods inaccessible and difficult to solve. Especially for the complex multibody dynamic system described by differential-algebraic equations, it is generally high-dimensional and strongly nonlinear, and the optimization calculation is more difficult. In this paper, focusing on optimization problems with discrete variables and mixed discrete-continuous variables, the continuous method for dynamic optimization of multibody systems is proposed. It converts the original problem into a continuous variable optimization problem, avoiding the inherent discontinuity and difficulty of discrete variables, so that the optimization problem can be solved by mature nonlinear programming tools. Two calculation formulas for the continuous method and their implementation are given based on the sigmoid function and nonlinear complementary problem (NCP) function, respectively. The validity and engineering practicability of the proposed method are demonstrated using two dynamic optimization examples of multibody systems with discrete and mixed variables. © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables
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Continuous methods for dynamic optimization of multibody systems with discrete and mixed variables

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