Control Lyapunov-Barrier function-based model predictive control of nonlinear systems

In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a cons...
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Autor*in:	Wu, Zhe [verfasserIn] Albalawi, Fahad [verfasserIn] Zhang, Zhihao [verfasserIn] Zhang, Junfeng [verfasserIn] Durand, Helen [verfasserIn] Christofides, Panagiotis D. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems

Übergeordnetes Werk:	Enthalten in: Automatica - Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963, 109
Übergeordnetes Werk:	volume:109

DOI / URN:	10.1016/j.automatica.2019.108508

Katalog-ID:	ELV002868741

Internformat


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245	1	0	\|a Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
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337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example.
650		4	\|a Process safety
650		4	\|a Process control
650		4	\|a Model predictive control
650		4	\|a Control Lyapunov-Barrier functions
650		4	\|a Nonlinear systems
700	1		\|a Albalawi, Fahad \|e verfasserin \|4 aut
700	1		\|a Zhang, Zhihao \|e verfasserin \|4 aut
700	1		\|a Zhang, Junfeng \|e verfasserin \|4 aut
700	1		\|a Durand, Helen \|e verfasserin \|4 aut
700	1		\|a Christofides, Panagiotis D. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Automatica \|d Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 \|g 109 \|h Online-Ressource \|w (DE-627)266886388 \|w (DE-600)1468509-7 \|w (DE-576)094478724 \|x 0005-1098 \|7 nnns
773	1	8	\|g volume:109
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
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_266
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
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
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
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
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
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 31.00 \|j Mathematik: Allgemeines
936	b	k	\|a 50.20 \|j Automatisierungstechnik
951			\|a AR
952			\|d 109

Indexfelder

author_variant	z w zw f a fa z z zz j z jz h d hd p d c pd pdc
matchkey_str	article:00051098:2019----::otolauobrirucinaemdlrdcieot
hierarchy_sort_str	2019
bklnumber	31.00 50.20
publishDate	2019
allfields	10.1016/j.automatica.2019.108508 doi (DE-627)ELV002868741 (ELSEVIER)S0005-1098(19)30369-3 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Wu, Zhe verfasserin aut Control Lyapunov-Barrier function-based model predictive control of nonlinear systems 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example. Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems Albalawi, Fahad verfasserin aut Zhang, Zhihao verfasserin aut Zhang, Junfeng verfasserin aut Durand, Helen verfasserin aut Christofides, Panagiotis D. verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 109 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 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_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 109
spelling	10.1016/j.automatica.2019.108508 doi (DE-627)ELV002868741 (ELSEVIER)S0005-1098(19)30369-3 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Wu, Zhe verfasserin aut Control Lyapunov-Barrier function-based model predictive control of nonlinear systems 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example. Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems Albalawi, Fahad verfasserin aut Zhang, Zhihao verfasserin aut Zhang, Junfeng verfasserin aut Durand, Helen verfasserin aut Christofides, Panagiotis D. verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 109 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 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_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 109
allfields_unstemmed	10.1016/j.automatica.2019.108508 doi (DE-627)ELV002868741 (ELSEVIER)S0005-1098(19)30369-3 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Wu, Zhe verfasserin aut Control Lyapunov-Barrier function-based model predictive control of nonlinear systems 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example. Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems Albalawi, Fahad verfasserin aut Zhang, Zhihao verfasserin aut Zhang, Junfeng verfasserin aut Durand, Helen verfasserin aut Christofides, Panagiotis D. verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 109 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 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_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 109
allfieldsGer	10.1016/j.automatica.2019.108508 doi (DE-627)ELV002868741 (ELSEVIER)S0005-1098(19)30369-3 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Wu, Zhe verfasserin aut Control Lyapunov-Barrier function-based model predictive control of nonlinear systems 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example. Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems Albalawi, Fahad verfasserin aut Zhang, Zhihao verfasserin aut Zhang, Junfeng verfasserin aut Durand, Helen verfasserin aut Christofides, Panagiotis D. verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 109 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 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_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 109
allfieldsSound	10.1016/j.automatica.2019.108508 doi (DE-627)ELV002868741 (ELSEVIER)S0005-1098(19)30369-3 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Wu, Zhe verfasserin aut Control Lyapunov-Barrier function-based model predictive control of nonlinear systems 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example. Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems Albalawi, Fahad verfasserin aut Zhang, Zhihao verfasserin aut Zhang, Junfeng verfasserin aut Durand, Helen verfasserin aut Christofides, Panagiotis D. verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 109 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 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_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 109
language	English
source	Enthalten in Automatica 109 volume:109
sourceStr	Enthalten in Automatica 109 volume:109
format_phy_str_mv	Article
bklname	Mathematik: Allgemeines Automatisierungstechnik
institution	findex.gbv.de
topic_facet	Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems
dewey-raw	000
isfreeaccess_bool	false
container_title	Automatica
authorswithroles_txt_mv	Wu, Zhe @@aut@@ Albalawi, Fahad @@aut@@ Zhang, Zhihao @@aut@@ Zhang, Junfeng @@aut@@ Durand, Helen @@aut@@ Christofides, Panagiotis D. @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	266886388
dewey-sort	0
id	ELV002868741
language_de	englisch
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author	Wu, Zhe
spellingShingle	Wu, Zhe ddc 000 bkl 31.00 bkl 50.20 misc Process safety misc Process control misc Model predictive control misc Control Lyapunov-Barrier functions misc Nonlinear systems Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
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topic_title	000 620 DE-600 31.00 bkl 50.20 bkl Control Lyapunov-Barrier function-based model predictive control of nonlinear systems Process safety Process control Model predictive control Control Lyapunov-Barrier functions Nonlinear systems
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title	Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
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title_full	Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
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author_browse	Wu, Zhe Albalawi, Fahad Zhang, Zhihao Zhang, Junfeng Durand, Helen Christofides, Panagiotis D.
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title_sort	control lyapunov-barrier function-based model predictive control of nonlinear systems
title_auth	Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
abstract	In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example.
abstractGer	In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example.
abstract_unstemmed	In this paper, we propose a Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. Specifically, considering the input constraints, a constrained Control Lyapunov-Barrier Function is initially employed to design an explicit control law and characterize a set of initial conditions, starting from which the solution of the nonlinear system is guaranteed to converge to the steady-state without entering a specified unsafe region in the state space. Then, the CLBF-MPC is proposed and is shown to be recursively feasible, and stabilizing and to ensure the avoidance of a set of states in state–space associated with unsafe operating conditions under sample-and-hold control action implementation. Finally, we demonstrate the efficacy of the proposed CLBF-MPC method through application to a chemical process example.
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title_short	Control Lyapunov-Barrier function-based model predictive control of nonlinear systems
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author2	Albalawi, Fahad Zhang, Zhihao Zhang, Junfeng Durand, Helen Christofides, Panagiotis D.
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doi_str	10.1016/j.automatica.2019.108508
up_date	2024-07-06T17:43:39.654Z
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Control Lyapunov-Barrier function-based model predictive control of nonlinear systems

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