Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities

This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the a...
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Gespeichert in:

Autor*in:	Zhang, Liuliu [verfasserIn] Zhu, Lingchen [verfasserIn] Hua, Changchun [verfasserIn] Qian, Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity

Übergeordnetes Werk:	Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 461, Seite 147-161
Übergeordnetes Werk:	volume:461 ; pages:147-161

DOI / URN:	10.1016/j.neucom.2021.07.043

Katalog-ID:	ELV006677703

Internformat


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520			\|a This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.
650		4	\|a Adaptive neural network control
650		4	\|a Interconnected time-delay system
650		4	\|a Full-state constraints
650		4	\|a Unknown measurement sensitivity
700	1		\|a Zhu, Lingchen \|e verfasserin \|4 aut
700	1		\|a Hua, Changchun \|e verfasserin \|4 aut
700	1		\|a Qian, Cheng \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neurocomputing \|d Amsterdam : Elsevier, 1989 \|g 461, Seite 147-161 \|h Online-Ressource \|w (DE-627)271176008 \|w (DE-600)1479006-3 \|w (DE-576)078412358 \|x 1872-8286 \|7 nnns
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912			\|a GBV_ILN_69
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912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
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912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2038
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912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
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912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
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912			\|a GBV_ILN_4324
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allfields	10.1016/j.neucom.2021.07.043 doi (DE-627)ELV006677703 (ELSEVIER)S0925-2312(21)01087-0 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Liuliu verfasserin aut Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method. Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity Zhu, Lingchen verfasserin aut Hua, Changchun verfasserin aut Qian, Cheng verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 461, Seite 147-161 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:461 pages:147-161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 461 147-161
spelling	10.1016/j.neucom.2021.07.043 doi (DE-627)ELV006677703 (ELSEVIER)S0925-2312(21)01087-0 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Liuliu verfasserin aut Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method. Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity Zhu, Lingchen verfasserin aut Hua, Changchun verfasserin aut Qian, Cheng verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 461, Seite 147-161 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:461 pages:147-161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 461 147-161
allfields_unstemmed	10.1016/j.neucom.2021.07.043 doi (DE-627)ELV006677703 (ELSEVIER)S0925-2312(21)01087-0 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Liuliu verfasserin aut Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method. Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity Zhu, Lingchen verfasserin aut Hua, Changchun verfasserin aut Qian, Cheng verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 461, Seite 147-161 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:461 pages:147-161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 461 147-161
allfieldsGer	10.1016/j.neucom.2021.07.043 doi (DE-627)ELV006677703 (ELSEVIER)S0925-2312(21)01087-0 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Liuliu verfasserin aut Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method. Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity Zhu, Lingchen verfasserin aut Hua, Changchun verfasserin aut Qian, Cheng verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 461, Seite 147-161 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:461 pages:147-161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 461 147-161
allfieldsSound	10.1016/j.neucom.2021.07.043 doi (DE-627)ELV006677703 (ELSEVIER)S0925-2312(21)01087-0 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Liuliu verfasserin aut Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method. Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity Zhu, Lingchen verfasserin aut Hua, Changchun verfasserin aut Qian, Cheng verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 461, Seite 147-161 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:461 pages:147-161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 461 147-161
language	English
source	Enthalten in Neurocomputing 461, Seite 147-161 volume:461 pages:147-161
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topic_facet	Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity
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authorswithroles_txt_mv	Zhang, Liuliu @@aut@@ Zhu, Lingchen @@aut@@ Hua, Changchun @@aut@@ Qian, Cheng @@aut@@
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author	Zhang, Liuliu
spellingShingle	Zhang, Liuliu ddc 610 bkl 54.72 misc Adaptive neural network control misc Interconnected time-delay system misc Full-state constraints misc Unknown measurement sensitivity Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
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topic_title	610 DE-600 54.72 bkl Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities Adaptive neural network control Interconnected time-delay system Full-state constraints Unknown measurement sensitivity
topic	ddc 610 bkl 54.72 misc Adaptive neural network control misc Interconnected time-delay system misc Full-state constraints misc Unknown measurement sensitivity
topic_unstemmed	ddc 610 bkl 54.72 misc Adaptive neural network control misc Interconnected time-delay system misc Full-state constraints misc Unknown measurement sensitivity
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title	Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
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title_full	Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
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title_sort	adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
title_auth	Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
abstract	This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.
abstractGer	This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.
abstract_unstemmed	This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.
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title_short	Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities
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up_date	2024-07-06T22:11:32.449Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV006677703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524130525.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2021.07.043</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV006677703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(21)01087-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Liuliu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive neural network control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Interconnected time-delay system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Full-state constraints</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unknown measurement sensitivity</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Lingchen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hua, Changchun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Cheng</subfield><subfield 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Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities

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