Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems

In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknow...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng, Xiaoqin [verfasserIn] Yang, Yucui [verfasserIn] Wei, Dongxu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems

Übergeordnetes Werk:	Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 428, Seite 12-18
Übergeordnetes Werk:	volume:428 ; pages:12-18

DOI / URN:	10.1016/j.neucom.2020.11.043

Katalog-ID:	ELV005379555

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520			\|a In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol.
650		4	\|a Heterogeneous multi-agent systems
650		4	\|a Adaptive control
650		4	\|a Distributed consensus
650		4	\|a Nonlinear systems
700	1		\|a Yang, Yucui \|e verfasserin \|4 aut
700	1		\|a Wei, Dongxu \|e verfasserin \|4 aut
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912			\|a GBV_ILN_95
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912			\|a GBV_ILN_110
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
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912			\|a GBV_ILN_2122
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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
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
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
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
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912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
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912			\|a GBV_ILN_4326
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912			\|a GBV_ILN_4334
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author_variant	x f xf y y yy d w dw
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allfields	10.1016/j.neucom.2020.11.043 doi (DE-627)ELV005379555 (ELSEVIER)S0925-2312(20)31836-1 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Feng, Xiaoqin verfasserin aut Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol. Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems Yang, Yucui verfasserin aut Wei, Dongxu verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 428, Seite 12-18 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:428 pages:12-18 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 428 12-18
spelling	10.1016/j.neucom.2020.11.043 doi (DE-627)ELV005379555 (ELSEVIER)S0925-2312(20)31836-1 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Feng, Xiaoqin verfasserin aut Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol. Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems Yang, Yucui verfasserin aut Wei, Dongxu verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 428, Seite 12-18 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:428 pages:12-18 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 428 12-18
allfields_unstemmed	10.1016/j.neucom.2020.11.043 doi (DE-627)ELV005379555 (ELSEVIER)S0925-2312(20)31836-1 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Feng, Xiaoqin verfasserin aut Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol. Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems Yang, Yucui verfasserin aut Wei, Dongxu verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 428, Seite 12-18 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:428 pages:12-18 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 428 12-18
allfieldsGer	10.1016/j.neucom.2020.11.043 doi (DE-627)ELV005379555 (ELSEVIER)S0925-2312(20)31836-1 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Feng, Xiaoqin verfasserin aut Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol. Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems Yang, Yucui verfasserin aut Wei, Dongxu verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 428, Seite 12-18 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:428 pages:12-18 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 428 12-18
allfieldsSound	10.1016/j.neucom.2020.11.043 doi (DE-627)ELV005379555 (ELSEVIER)S0925-2312(20)31836-1 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Feng, Xiaoqin verfasserin aut Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol. Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems Yang, Yucui verfasserin aut Wei, Dongxu verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 428, Seite 12-18 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:428 pages:12-18 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 428 12-18
language	English
source	Enthalten in Neurocomputing 428, Seite 12-18 volume:428 pages:12-18
sourceStr	Enthalten in Neurocomputing 428, Seite 12-18 volume:428 pages:12-18
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bklname	Künstliche Intelligenz
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topic_facet	Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems
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container_title	Neurocomputing
authorswithroles_txt_mv	Feng, Xiaoqin @@aut@@ Yang, Yucui @@aut@@ Wei, Dongxu @@aut@@
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author	Feng, Xiaoqin
spellingShingle	Feng, Xiaoqin ddc 610 bkl 54.72 misc Heterogeneous multi-agent systems misc Adaptive control misc Distributed consensus misc Nonlinear systems Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
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topic_title	610 DE-600 54.72 bkl Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems Heterogeneous multi-agent systems Adaptive control Distributed consensus Nonlinear systems
topic	ddc 610 bkl 54.72 misc Heterogeneous multi-agent systems misc Adaptive control misc Distributed consensus misc Nonlinear systems
topic_unstemmed	ddc 610 bkl 54.72 misc Heterogeneous multi-agent systems misc Adaptive control misc Distributed consensus misc Nonlinear systems
topic_browse	ddc 610 bkl 54.72 misc Heterogeneous multi-agent systems misc Adaptive control misc Distributed consensus misc Nonlinear systems
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title	Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
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title_full	Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
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title_sort	adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
title_auth	Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
abstract	In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol.
abstractGer	In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol.
abstract_unstemmed	In this paper, the consensus problem for a class of nonlinear multi-agent systems is investigated, where each agent is described by a first- or second-order differential equation. Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. Finally, a demonstrative example is presented in the simulation studies to illustrative the effectiveness of the given protocol.
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title_short	Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems
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author2	Yang, Yucui Wei, Dongxu
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doi_str	10.1016/j.neucom.2020.11.043
up_date	2024-07-06T17:46:24.968Z
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Due to the presence of unknown control gain, unknown parameter vector in the dynamical systems of agents, and the unknown interaction topology information, an adaptive consensus protocol is proposed. It is proven that the asymptotic consensus of the nonlinear multi-agent systems can be achieved without using any global information via the given protocol. 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Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems

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