Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach

Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or a...
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Autor*in:	Banapour, Elham [verfasserIn] Bagheri, Peyman [verfasserIn] Hashemzadeh, Farzad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Stochastic model predictive control Constraint handling Noise Convex optimization

Anmerkung:	© The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Iranian journal of science and technology - Springer International Publishing, 1999, 48(2024), 3 vom: 23. März, Seite 1199-1208
Übergeordnetes Werk:	volume:48 ; year:2024 ; number:3 ; day:23 ; month:03 ; pages:1199-1208

Links:	Volltext

DOI / URN:	10.1007/s40998-024-00703-3

Katalog-ID:	SPR05692318X

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allfields	10.1007/s40998-024-00703-3 doi (DE-627)SPR05692318X (SPR)s40998-024-00703-3-e DE-627 ger DE-627 rakwb eng 500 600 VZ 500 600 VZ 53.00 bkl Banapour, Elham verfasserin aut Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. Stochastic model predictive control (dpeaa)DE-He213 Constraint handling (dpeaa)DE-He213 Noise (dpeaa)DE-He213 Convex optimization (dpeaa)DE-He213 Bagheri, Peyman verfasserin (orcid)0000-0002-9341-8814 aut Hashemzadeh, Farzad verfasserin aut Enthalten in Iranian journal of science and technology Springer International Publishing, 1999 48(2024), 3 vom: 23. März, Seite 1199-1208 (DE-627)844130222 (DE-600)2842937-0 2364-1827 nnns volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208 https://dx.doi.org/10.1007/s40998-024-00703-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 VZ AR 48 2024 3 23 03 1199-1208
spelling	10.1007/s40998-024-00703-3 doi (DE-627)SPR05692318X (SPR)s40998-024-00703-3-e DE-627 ger DE-627 rakwb eng 500 600 VZ 500 600 VZ 53.00 bkl Banapour, Elham verfasserin aut Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. Stochastic model predictive control (dpeaa)DE-He213 Constraint handling (dpeaa)DE-He213 Noise (dpeaa)DE-He213 Convex optimization (dpeaa)DE-He213 Bagheri, Peyman verfasserin (orcid)0000-0002-9341-8814 aut Hashemzadeh, Farzad verfasserin aut Enthalten in Iranian journal of science and technology Springer International Publishing, 1999 48(2024), 3 vom: 23. März, Seite 1199-1208 (DE-627)844130222 (DE-600)2842937-0 2364-1827 nnns volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208 https://dx.doi.org/10.1007/s40998-024-00703-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 VZ AR 48 2024 3 23 03 1199-1208
allfields_unstemmed	10.1007/s40998-024-00703-3 doi (DE-627)SPR05692318X (SPR)s40998-024-00703-3-e DE-627 ger DE-627 rakwb eng 500 600 VZ 500 600 VZ 53.00 bkl Banapour, Elham verfasserin aut Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. Stochastic model predictive control (dpeaa)DE-He213 Constraint handling (dpeaa)DE-He213 Noise (dpeaa)DE-He213 Convex optimization (dpeaa)DE-He213 Bagheri, Peyman verfasserin (orcid)0000-0002-9341-8814 aut Hashemzadeh, Farzad verfasserin aut Enthalten in Iranian journal of science and technology Springer International Publishing, 1999 48(2024), 3 vom: 23. März, Seite 1199-1208 (DE-627)844130222 (DE-600)2842937-0 2364-1827 nnns volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208 https://dx.doi.org/10.1007/s40998-024-00703-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 VZ AR 48 2024 3 23 03 1199-1208
allfieldsGer	10.1007/s40998-024-00703-3 doi (DE-627)SPR05692318X (SPR)s40998-024-00703-3-e DE-627 ger DE-627 rakwb eng 500 600 VZ 500 600 VZ 53.00 bkl Banapour, Elham verfasserin aut Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. Stochastic model predictive control (dpeaa)DE-He213 Constraint handling (dpeaa)DE-He213 Noise (dpeaa)DE-He213 Convex optimization (dpeaa)DE-He213 Bagheri, Peyman verfasserin (orcid)0000-0002-9341-8814 aut Hashemzadeh, Farzad verfasserin aut Enthalten in Iranian journal of science and technology Springer International Publishing, 1999 48(2024), 3 vom: 23. März, Seite 1199-1208 (DE-627)844130222 (DE-600)2842937-0 2364-1827 nnns volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208 https://dx.doi.org/10.1007/s40998-024-00703-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 VZ AR 48 2024 3 23 03 1199-1208
allfieldsSound	10.1007/s40998-024-00703-3 doi (DE-627)SPR05692318X (SPR)s40998-024-00703-3-e DE-627 ger DE-627 rakwb eng 500 600 VZ 500 600 VZ 53.00 bkl Banapour, Elham verfasserin aut Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. Stochastic model predictive control (dpeaa)DE-He213 Constraint handling (dpeaa)DE-He213 Noise (dpeaa)DE-He213 Convex optimization (dpeaa)DE-He213 Bagheri, Peyman verfasserin (orcid)0000-0002-9341-8814 aut Hashemzadeh, Farzad verfasserin aut Enthalten in Iranian journal of science and technology Springer International Publishing, 1999 48(2024), 3 vom: 23. März, Seite 1199-1208 (DE-627)844130222 (DE-600)2842937-0 2364-1827 nnns volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208 https://dx.doi.org/10.1007/s40998-024-00703-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 VZ AR 48 2024 3 23 03 1199-1208
language	English
source	Enthalten in Iranian journal of science and technology 48(2024), 3 vom: 23. März, Seite 1199-1208 volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208
sourceStr	Enthalten in Iranian journal of science and technology 48(2024), 3 vom: 23. März, Seite 1199-1208 volume:48 year:2024 number:3 day:23 month:03 pages:1199-1208
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Stochastic model predictive control Constraint handling Noise Convex optimization
dewey-raw	500
isfreeaccess_bool	false
container_title	Iranian journal of science and technology
authorswithroles_txt_mv	Banapour, Elham @@aut@@ Bagheri, Peyman @@aut@@ Hashemzadeh, Farzad @@aut@@
publishDateDaySort_date	2024-03-23T00:00:00Z
hierarchy_top_id	844130222
dewey-sort	3500
id	SPR05692318X
language_de	englisch
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author	Banapour, Elham
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title_sort	output feedback stochastic model predictive control for linear systems with convex optimization approach
title_auth	Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach
abstract	Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Shiraz University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	3
title_short	Output Feedback Stochastic Model Predictive Control for Linear Systems with Convex Optimization Approach
url	https://dx.doi.org/10.1007/s40998-024-00703-3
remote_bool	true
author2	Bagheri, Peyman Hashemzadeh, Farzad
author2Str	Bagheri, Peyman Hashemzadeh, Farzad
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doi_str	10.1007/s40998-024-00703-3
up_date	2024-08-10T04:49:54.751Z
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