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...
Ausführliche Beschreibung
Autor*in: |
Zhang, Liuliu [verfasserIn] Zhu, Lingchen [verfasserIn] Hua, Changchun [verfasserIn] Qian, Cheng [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
Adaptive neural network control |
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Übergeordnetes Werk: |
Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 461, Seite 147-161 |
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Übergeordnetes Werk: |
volume:461 ; pages:147-161 |
DOI / URN: |
10.1016/j.neucom.2021.07.043 |
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Katalog-ID: |
ELV006677703 |
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245 | 1 | 0 | |a 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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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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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 |
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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 |
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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 |
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Zhang, Liuliu Zhu, Lingchen Hua, Changchun Qian, Cheng |
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Zhang, Liuliu |
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10.1016/j.neucom.2021.07.043 |
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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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author2 |
Zhu, Lingchen Hua, Changchun Qian, Cheng |
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doi_str |
10.1016/j.neucom.2021.07.043 |
up_date |
2024-07-06T22:11:32.449Z |
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