Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system

Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance o...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ting, Jung-Chu [verfasserIn] Chen, Der-Fa

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Synchronous reluctance motor Recurrent Hermite polynomial neural network Lyapunov stability Continuously variable transmission

Anmerkung:	© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Übergeordnetes Werk:	Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 32(2018), 9 vom: Sept., Seite 4399-4412
Übergeordnetes Werk:	volume:32 ; year:2018 ; number:9 ; month:09 ; pages:4399-4412

Links:	Volltext

DOI / URN:	10.1007/s12206-018-0838-9

Katalog-ID:	SPR025340255

Internformat


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245	1	0	\|a Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
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520			\|a Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations.
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912			\|a GBV_ILN_602
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author_variant	j c t jct d f c dfc
matchkey_str	article:19763824:2018----::oemnldeomdeurnhrieoyoilerlewrcnrlytmpleicni
hierarchy_sort_str	2018
publishDate	2018
allfields	10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 32 2018 9 09 4399-4412
spelling	10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 32 2018 9 09 4399-4412
allfields_unstemmed	10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 32 2018 9 09 4399-4412
allfieldsGer	10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 32 2018 9 09 4399-4412
allfieldsSound	10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 32 2018 9 09 4399-4412
language	English
source	Enthalten in Journal of mechanical science and technology 32(2018), 9 vom: Sept., Seite 4399-4412 volume:32 year:2018 number:9 month:09 pages:4399-4412
sourceStr	Enthalten in Journal of mechanical science and technology 32(2018), 9 vom: Sept., Seite 4399-4412 volume:32 year:2018 number:9 month:09 pages:4399-4412
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Synchronous reluctance motor Recurrent Hermite polynomial neural network Lyapunov stability Continuously variable transmission
isfreeaccess_bool	false
container_title	Journal of mechanical science and technology
authorswithroles_txt_mv	Ting, Jung-Chu @@aut@@ Chen, Der-Fa @@aut@@
publishDateDaySort_date	2018-09-01T00:00:00Z
hierarchy_top_id	58714016X
id	SPR025340255
language_de	englisch
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author	Ting, Jung-Chu
spellingShingle	Ting, Jung-Chu misc Synchronous reluctance motor misc Recurrent Hermite polynomial neural network misc Lyapunov stability misc Continuously variable transmission Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
authorStr	Ting, Jung-Chu
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topic_title	Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213
topic	misc Synchronous reluctance motor misc Recurrent Hermite polynomial neural network misc Lyapunov stability misc Continuously variable transmission
topic_unstemmed	misc Synchronous reluctance motor misc Recurrent Hermite polynomial neural network misc Lyapunov stability misc Continuously variable transmission
topic_browse	misc Synchronous reluctance motor misc Recurrent Hermite polynomial neural network misc Lyapunov stability misc Continuously variable transmission
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
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title_full	Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
author_sort	Ting, Jung-Chu
journal	Journal of mechanical science and technology
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author_browse	Ting, Jung-Chu Chen, Der-Fa
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author-letter	Ting, Jung-Chu
doi_str_mv	10.1007/s12206-018-0838-9
title_sort	novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
title_auth	Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
abstract	Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstractGer	Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstract_unstemmed	Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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title_short	Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
url	https://dx.doi.org/10.1007/s12206-018-0838-9
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author2	Chen, Der-Fa
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doi_str	10.1007/s12206-018-0838-9
up_date	2024-07-03T15:23:35.566Z
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The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. 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