Stochastic MPC with offline uncertainty sampling
For discrete-time linear systems subject to multiplicative disturbance described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offline sampling approach in the design phase inst...
Saved in:
| Published in | Automatica (Oxford) Vol. 81; pp. 176 - 183 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.07.2017
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/j.automatica.2017.03.031 |
Cover
| Abstract | For discrete-time linear systems subject to multiplicative disturbance described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offline sampling approach in the design phase instead of online scenario generation. The paper highlights the structural difference between online and offline sampling and provides rigorous bounds on the number of samples needed to guarantee chance constraint satisfaction. The approach does not only significantly speed up the online computation, but furthermore allows to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Under mild assumptions, asymptotic stability of the origin can be established. |
|---|---|
| AbstractList | For discrete-time linear systems subject to multiplicative disturbance described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offline sampling approach in the design phase instead of online scenario generation. The paper highlights the structural difference between online and offline sampling and provides rigorous bounds on the number of samples needed to guarantee chance constraint satisfaction. The approach does not only significantly speed up the online computation, but furthermore allows to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Under mild assumptions, asymptotic stability of the origin can be established. |
| Author | Lorenzen, Matthias Tempo, Roberto Allgöwer, Frank Dabbene, Fabrizio |
| Author_xml | – sequence: 1 givenname: Matthias surname: Lorenzen fullname: Lorenzen, Matthias email: matthias.lorenzen@ist.uni-stuttgart.de organization: Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany – sequence: 2 givenname: Fabrizio surname: Dabbene fullname: Dabbene, Fabrizio email: fabrizio.dabbene@ieiit.cnr.it organization: CNR-IEIIT, Politecnico di Torino, Italy – sequence: 3 givenname: Roberto surname: Tempo fullname: Tempo, Roberto email: roberto.tempo@polito.it organization: CNR-IEIIT, Politecnico di Torino, Italy – sequence: 4 givenname: Frank surname: Allgöwer fullname: Allgöwer, Frank email: frank.allgower@ist.uni-stuttgart.de organization: Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany |
| BookMark | eNqNkNtKAzEQhoNUsFXfYV9g10yyh_RG0OIJKgrqdchOEpvSbkqSKn17UyoI3igMDDPM_8E3EzIa_GAIKYBWQKG9WFZqm_xaJYeqYhS6ivJccETGIDpeMsHbERlTSpsS6FSckEmMyzzWINiY0JfkcaFijhePz7Pi06VF4a1ducEU2wFNSMoNaVdEtd7k5fsZObZqFc35dz8lb7c3r7P7cv509zC7mpfIQaSSMzRCcVtjjaxnTd_3CExPNQreMLC6r5G20Pact8CVtlxMNUOs24Z1XdPwUyIOXAw-xmCs3AS3VmEngcq9uVzKH3O5N5eU54IcvfwVRZfymR9SUG71H8D1AWCy4IczQUZ0Jj9Du2AwSe3d35AvfU9-VQ |
| CitedBy_id | crossref_primary_10_1049_cth2_12070 crossref_primary_10_1007_s11071_022_07592_3 crossref_primary_10_1002_rnc_3912 crossref_primary_10_1016_j_isatra_2019_01_013 crossref_primary_10_1016_j_jfranklin_2022_02_004 crossref_primary_10_1016_j_automatica_2023_111146 crossref_primary_10_9746_jcmsi_11_14 crossref_primary_10_1016_j_ifacol_2020_12_362 crossref_primary_10_1098_rsta_2020_0371 crossref_primary_10_1016_j_automatica_2020_109402 crossref_primary_10_1109_TCST_2018_2879938 crossref_primary_10_1016_j_automatica_2018_03_010 crossref_primary_10_1146_annurev_control_060117_105215 crossref_primary_10_1109_TAC_2023_3321375 crossref_primary_10_1016_j_automatica_2022_110807 crossref_primary_10_1109_ACCESS_2022_3160709 crossref_primary_10_1109_TAC_2021_3056356 crossref_primary_10_1016_j_ifacol_2023_10_1641 crossref_primary_10_1109_ACCESS_2021_3098169 crossref_primary_10_1109_OJCSYS_2023_3291596 crossref_primary_10_1080_00207721_2023_2268234 crossref_primary_10_1109_TCST_2023_3291556 crossref_primary_10_1109_LCSYS_2019_2918763 crossref_primary_10_1016_j_ifacol_2022_09_041 crossref_primary_10_1016_j_jfranklin_2023_05_012 crossref_primary_10_1109_LCSYS_2022_3182643 crossref_primary_10_1109_LCSYS_2018_2845546 crossref_primary_10_1109_TSTE_2024_3512997 crossref_primary_10_1016_j_automatica_2019_108702 crossref_primary_10_1016_j_ifacol_2022_09_334 crossref_primary_10_1016_j_automatica_2021_110108 crossref_primary_10_1146_annurev_control_090419_075625 crossref_primary_10_1016_j_ins_2018_05_021 crossref_primary_10_1109_TAC_2016_2625048 crossref_primary_10_1109_LCSYS_2019_2949194 crossref_primary_10_1109_LCSYS_2024_3445723 crossref_primary_10_1002_rnc_5696 crossref_primary_10_1515_auto_2021_0025 crossref_primary_10_1109_TSMC_2024_3444039 |
| Cites_doi | 10.1137/07069821X 10.1016/j.automatica.2014.11.004 10.3182/20140824-6-ZA-1003.00696 10.1016/j.automatica.2014.10.128 10.1137/120878719 10.1109/CDC.2015.7402994 10.1109/TAC.2008.2010886 10.1016/j.sysconle.2007.11.003 10.1109/CDC.2014.7040135 10.1109/TAC.2006.875041 10.1109/ACC.2015.7170855 10.1016/j.jprocont.2016.03.005 10.1109/ECC.2014.6862498 10.1016/j.automatica.2013.02.060 10.1016/j.automatica.2014.10.035 10.23919/ECC.2013.6669862 10.1109/TAC.2016.2625048 10.1007/s10957-010-9754-6 10.1109/TAC.2009.2031207 10.1137/090773490 10.1109/ACC.2014.6858851 |
| ContentType | Journal Article |
| Copyright | 2017 Elsevier Ltd |
| Copyright_xml | – notice: 2017 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.automatica.2017.03.031 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-2836 |
| EndPage | 183 |
| ExternalDocumentID | 10_1016_j_automatica_2017_03_031 S0005109817301619 |
| GrantInformation_xml | – fundername: German Research Foundation (DFG) grantid: EXC 310/2 funderid: http://dx.doi.org/10.13039/501100001659 – fundername: CNR Joint International Lab COOPS |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 23N 3R3 4.4 457 4G. 5GY 5VS 6TJ 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO ABDEX ABFNM ABFRF ABJNI ABMAC ABUCO ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ADBBV ADEZE ADIYS ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHPGS AI. AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ APLSM ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA K-O KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ RXW SBC SDF SDG SDP SES SET SEW SPC SPCBC SSB SSD SST SSZ T5K T9H TAE TN5 VH1 WH7 WUQ X6Y XFK XPP ZMT ~G- AATTM AAXKI AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH |
| ID | FETCH-LOGICAL-c318t-32ce8a3f4c4c2b25bbbc12d9dc83521fdb4c0616b33613adf389d2cc465277553 |
| IEDL.DBID | AIKHN |
| ISSN | 0005-1098 |
| IngestDate | Thu Apr 24 23:11:27 EDT 2025 Tue Jul 01 00:43:48 EDT 2025 Fri Feb 23 02:23:49 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Receding horizon control Stochastic control Stochastic model predictive control Data-based control Control of constrained systems |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c318t-32ce8a3f4c4c2b25bbbc12d9dc83521fdb4c0616b33613adf389d2cc465277553 |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1016_j_automatica_2017_03_031 crossref_citationtrail_10_1016_j_automatica_2017_03_031 elsevier_sciencedirect_doi_10_1016_j_automatica_2017_03_031 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | July 2017 2017-07-00 |
| PublicationDateYYYYMMDD | 2017-07-01 |
| PublicationDate_xml | – month: 07 year: 2017 text: July 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | Automatica (Oxford) |
| PublicationYear | 2017 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Mesbah, A., Streif, S., Findeisen, R., & Braatz, R.D. (2014). Stochastic nonlinear model predictive control with probabilistic constraints. In Herceg, M., Kvasnica, M., Jones, C.N., & Morari, M. (2013). Multi-parametric toolbox 3.0. In (pp. 2413–2419). Farina, Giulioni, Scattolini (br000060) 2016; 44 Lorenzen, M., Allgöwer, F., Dabbene, F., & Tempo, R. (2015b). Scenario-based stochastic MPC with guaranteed recursive feasibility. In Alamo, Tempo, Luque, Ramirez (br000010) 2015; 52 Blanchini, Miani (br000020) 2015 Fleming, J., Cannon, M., & Kouvaritakis, B. (2014). Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions. In Primbs, Sung (br000110) 2009; 54 Zhang, X., Grammatico, S., Schildbach, G., Goulart, P., & Lygeros, J. (2014). On the sample size of randomized MPC for chance-constrained systems with application to building climate control. In Lorenzen, Dabbene, Tempo, Allgöwer (br000095) 2017 Tempo, Calafiore, Dabbene (br000125) 2013 Kerrigan (br000075) 2000 Calafiore (br000030) 2010; 20 (pp. 4783–4788). Kouvaritakis, Cannon (br000080) 2016 (pp. 478–783). Campi, Garatti (br000050) 2011; 148 Boyd, El Ghaoui, Feron, Balakrishnan (br000025) 1994 Calafiore, Fagiano (br000040) 2013; 49 Deori, L., Garatti, S., & Prandini, M. (2014). Computational approaches to robust model predictive control: A comparative analysis. In (pp. 10820–10825). Alamo, Tempo, Ramírez, Camacho (br000015) 2008; 57 Schildbach, Fagiano, Morari (br000120) 2013; 23 Campi, Garatti (br000045) 2008; 19 Alamo, Tempo, Camacho (br000005) 2009; 54 Calafiore, Campi (br000035) 2006; 51 Lorenzen, M., Allgöwer, F., Dabbene, F., & Tempo, R. (2015a). An improved constraint-tightening approach for stochastic MPC. In (pp. 502–510). Mayne (br000100) 2014; 50 Schildbach, Fagiano, Frei, Morari (br000115) 2014; 50 (pp. 4958–4963). (pp. 944–949). Alamo (10.1016/j.automatica.2017.03.031_br000005) 2009; 54 Tempo (10.1016/j.automatica.2017.03.031_br000125) 2013 Alamo (10.1016/j.automatica.2017.03.031_br000015) 2008; 57 Calafiore (10.1016/j.automatica.2017.03.031_br000040) 2013; 49 Blanchini (10.1016/j.automatica.2017.03.031_br000020) 2015 Alamo (10.1016/j.automatica.2017.03.031_br000010) 2015; 52 Kerrigan (10.1016/j.automatica.2017.03.031_br000075) 2000 Schildbach (10.1016/j.automatica.2017.03.031_br000115) 2014; 50 10.1016/j.automatica.2017.03.031_br000065 Boyd (10.1016/j.automatica.2017.03.031_br000025) 1994 Campi (10.1016/j.automatica.2017.03.031_br000050) 2011; 148 10.1016/j.automatica.2017.03.031_br000085 Lorenzen (10.1016/j.automatica.2017.03.031_br000095) 2017 Calafiore (10.1016/j.automatica.2017.03.031_br000035) 2006; 51 Campi (10.1016/j.automatica.2017.03.031_br000045) 2008; 19 10.1016/j.automatica.2017.03.031_br000105 10.1016/j.automatica.2017.03.031_br000055 Farina (10.1016/j.automatica.2017.03.031_br000060) 2016; 44 Primbs (10.1016/j.automatica.2017.03.031_br000110) 2009; 54 10.1016/j.automatica.2017.03.031_br000130 Kouvaritakis (10.1016/j.automatica.2017.03.031_br000080) 2016 Calafiore (10.1016/j.automatica.2017.03.031_br000030) 2010; 20 10.1016/j.automatica.2017.03.031_br000070 Mayne (10.1016/j.automatica.2017.03.031_br000100) 2014; 50 10.1016/j.automatica.2017.03.031_br000090 Schildbach (10.1016/j.automatica.2017.03.031_br000120) 2013; 23 |
| References_xml | – volume: 20 start-page: 3427 year: 2010 end-page: 3464 ident: br000030 article-title: Random convex programs publication-title: SIAM Journal on Optimization – year: 2000 ident: br000075 article-title: Robust constraint satisfaction: Invariant sets and predictive control – reference: Deori, L., Garatti, S., & Prandini, M. (2014). Computational approaches to robust model predictive control: A comparative analysis. In – year: 2016 ident: br000080 article-title: Model predictive control: Classical, robust and stochastic – reference: Zhang, X., Grammatico, S., Schildbach, G., Goulart, P., & Lygeros, J. (2014). On the sample size of randomized MPC for chance-constrained systems with application to building climate control. In – volume: 57 start-page: 474 year: 2008 end-page: 481 ident: br000015 article-title: A new vertex result for robustness problems with interval matrix uncertainty publication-title: Systems & Control Letters – volume: 50 start-page: 2967 year: 2014 end-page: 2986 ident: br000100 article-title: Model predictive control: Recent developments and future promise publication-title: Automatica – reference: Herceg, M., Kvasnica, M., Jones, C.N., & Morari, M. (2013). Multi-parametric toolbox 3.0. In – reference: (pp. 2413–2419). – year: 1994 ident: br000025 article-title: Linear matrix inequalities in system and control theory, Vol. 15 – reference: (pp. 4783–4788). – reference: Fleming, J., Cannon, M., & Kouvaritakis, B. (2014). Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions. In – volume: 52 start-page: 160 year: 2015 end-page: 172 ident: br000010 article-title: Randomized methods for design of uncertain systems: Sample complexity and sequential algorithms publication-title: Automatica – volume: 54 start-page: 2545 year: 2009 end-page: 2559 ident: br000005 article-title: Randomized strategies for probabilistic solutions of uncertain feasibility and optimization problems publication-title: IEEE Transactions on Automatic Control – reference: (pp. 478–783). – volume: 23 start-page: 2479 year: 2013 end-page: 2501 ident: br000120 article-title: Randomized solutions to convex programs with multiple chance constraints publication-title: SIAM Journal on Optimization – volume: 148 start-page: 257 year: 2011 end-page: 280 ident: br000050 article-title: A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality publication-title: Journal of Optimization Theory and Applications – volume: 54 start-page: 221 year: 2009 end-page: 230 ident: br000110 article-title: Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise publication-title: IEEE Transactions on Automatic Control – year: 2013 ident: br000125 article-title: Randomized algorithms for analysis and control of uncertain systems: With applications – reference: Lorenzen, M., Allgöwer, F., Dabbene, F., & Tempo, R. (2015a). An improved constraint-tightening approach for stochastic MPC. In – reference: Lorenzen, M., Allgöwer, F., Dabbene, F., & Tempo, R. (2015b). Scenario-based stochastic MPC with guaranteed recursive feasibility. In – year: 2015 ident: br000020 publication-title: Set-theoretic methods in control – reference: (pp. 944–949). – volume: 49 start-page: 1861 year: 2013 end-page: 1866 ident: br000040 article-title: Stochastic model predictive control of LPV systems via scenario optimization publication-title: Automatica – volume: 19 start-page: 1211 year: 2008 end-page: 1230 ident: br000045 article-title: The exact feasibility of randomized solutions of uncertain convex programs publication-title: SIAM Journal on Optimization – volume: 44 start-page: 53 year: 2016 end-page: 67 ident: br000060 article-title: Stochastic linear model predictive control with chance constraints - a review publication-title: Journal of Process Control – year: 2017 ident: br000095 article-title: Constraint-tightening and stability in stochastic model predictive control publication-title: IEEE Transactions on Automatic Control – reference: (pp. 502–510). – volume: 51 start-page: 742 year: 2006 end-page: 753 ident: br000035 article-title: The scenario approach to robust control design publication-title: IEEE Transactions on Automatic Control – reference: Mesbah, A., Streif, S., Findeisen, R., & Braatz, R.D. (2014). Stochastic nonlinear model predictive control with probabilistic constraints. In – reference: (pp. 4958–4963). – volume: 50 start-page: 3009 year: 2014 end-page: 3018 ident: br000115 article-title: The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations publication-title: Automatica – reference: , (pp. 10820–10825). – year: 2000 ident: 10.1016/j.automatica.2017.03.031_br000075 – volume: 19 start-page: 1211 issue: 3 year: 2008 ident: 10.1016/j.automatica.2017.03.031_br000045 article-title: The exact feasibility of randomized solutions of uncertain convex programs publication-title: SIAM Journal on Optimization doi: 10.1137/07069821X – volume: 52 start-page: 160 year: 2015 ident: 10.1016/j.automatica.2017.03.031_br000010 article-title: Randomized methods for design of uncertain systems: Sample complexity and sequential algorithms publication-title: Automatica doi: 10.1016/j.automatica.2014.11.004 – ident: 10.1016/j.automatica.2017.03.031_br000055 doi: 10.3182/20140824-6-ZA-1003.00696 – volume: 50 start-page: 2967 issue: 12 year: 2014 ident: 10.1016/j.automatica.2017.03.031_br000100 article-title: Model predictive control: Recent developments and future promise publication-title: Automatica doi: 10.1016/j.automatica.2014.10.128 – volume: 23 start-page: 2479 issue: 4 year: 2013 ident: 10.1016/j.automatica.2017.03.031_br000120 article-title: Randomized solutions to convex programs with multiple chance constraints publication-title: SIAM Journal on Optimization doi: 10.1137/120878719 – ident: 10.1016/j.automatica.2017.03.031_br000090 doi: 10.1109/CDC.2015.7402994 – volume: 54 start-page: 221 issue: 2 year: 2009 ident: 10.1016/j.automatica.2017.03.031_br000110 article-title: Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2008.2010886 – volume: 57 start-page: 474 issue: 6 year: 2008 ident: 10.1016/j.automatica.2017.03.031_br000015 article-title: A new vertex result for robustness problems with interval matrix uncertainty publication-title: Systems & Control Letters doi: 10.1016/j.sysconle.2007.11.003 – ident: 10.1016/j.automatica.2017.03.031_br000065 doi: 10.1109/CDC.2014.7040135 – volume: 51 start-page: 742 issue: 5 year: 2006 ident: 10.1016/j.automatica.2017.03.031_br000035 article-title: The scenario approach to robust control design publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2006.875041 – year: 1994 ident: 10.1016/j.automatica.2017.03.031_br000025 – ident: 10.1016/j.automatica.2017.03.031_br000085 doi: 10.1109/ACC.2015.7170855 – volume: 44 start-page: 53 year: 2016 ident: 10.1016/j.automatica.2017.03.031_br000060 article-title: Stochastic linear model predictive control with chance constraints - a review publication-title: Journal of Process Control doi: 10.1016/j.jprocont.2016.03.005 – year: 2015 ident: 10.1016/j.automatica.2017.03.031_br000020 – ident: 10.1016/j.automatica.2017.03.031_br000130 doi: 10.1109/ECC.2014.6862498 – volume: 49 start-page: 1861 issue: 6 year: 2013 ident: 10.1016/j.automatica.2017.03.031_br000040 article-title: Stochastic model predictive control of LPV systems via scenario optimization publication-title: Automatica doi: 10.1016/j.automatica.2013.02.060 – year: 2013 ident: 10.1016/j.automatica.2017.03.031_br000125 – volume: 50 start-page: 3009 issue: 12 year: 2014 ident: 10.1016/j.automatica.2017.03.031_br000115 article-title: The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations publication-title: Automatica doi: 10.1016/j.automatica.2014.10.035 – ident: 10.1016/j.automatica.2017.03.031_br000070 doi: 10.23919/ECC.2013.6669862 – year: 2017 ident: 10.1016/j.automatica.2017.03.031_br000095 article-title: Constraint-tightening and stability in stochastic model predictive control publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2016.2625048 – year: 2016 ident: 10.1016/j.automatica.2017.03.031_br000080 – volume: 148 start-page: 257 issue: 2 year: 2011 ident: 10.1016/j.automatica.2017.03.031_br000050 article-title: A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality publication-title: Journal of Optimization Theory and Applications doi: 10.1007/s10957-010-9754-6 – volume: 54 start-page: 2545 issue: 11 year: 2009 ident: 10.1016/j.automatica.2017.03.031_br000005 article-title: Randomized strategies for probabilistic solutions of uncertain feasibility and optimization problems publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2009.2031207 – volume: 20 start-page: 3427 issue: 6 year: 2010 ident: 10.1016/j.automatica.2017.03.031_br000030 article-title: Random convex programs publication-title: SIAM Journal on Optimization doi: 10.1137/090773490 – ident: 10.1016/j.automatica.2017.03.031_br000105 doi: 10.1109/ACC.2014.6858851 |
| SSID | ssj0004182 |
| Score | 2.4698377 |
| Snippet | For discrete-time linear systems subject to multiplicative disturbance described by random variables, we develop a sampling-based Stochastic Model Predictive... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 176 |
| SubjectTerms | Control of constrained systems Data-based control Receding horizon control Stochastic control Stochastic model predictive control |
| Title | Stochastic MPC with offline uncertainty sampling |
| URI | https://dx.doi.org/10.1016/j.automatica.2017.03.031 |
| Volume | 81 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFA9zu-hB_MT5MXrwWtckTZrgaQzHVDYEHexW8tHoRNah3cGLf7tJm7oJgoLQSwoPmteX93skv_cLAOeES6Jt9IYiikVoV6IOJdM0ZBoarXiS6fJEdzSmw0l8MyXTBujXvTCOVulzf5XTy2zt33S9N7uL2cz1-LqA4gy6IKVO-rOFMKekCVq969vheNUeCVklGl6KbnLmCT0VzUssi7wUR3UiRDApFU8x_Bml1pBnsAO2fckY9Kqv2gWNbL4HttaEBPdBdF_k6kk4zeVgdNcP3O5qkBvjasjAIld17l-8B2_CMcjnjwdgMrh66A9DfxdCqOyqK0KMVMYENrGKFZKISCkVRJpr5Uoo61cZKwvNVGJsAVpoYwsRjZSKKUFJQgg-BM15Ps-O3Nyp4BE1VBti4TvjUPDYMJUIlkghUBsk9dxT5YXC3X0VL2nNCHtOV15LndfSCNsHtgH8slxUYhl_sLms3Zt--_Gpzem_Wh__y_oEbLpRxb49Bc3idZmd2RqjkB2wcfEBOz6SPgFvDtIN |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwGA1DH9QH8Yrz2gdf65bm0gSfZDimbkNwg72FXBqdSDu0e_DF327Si5sgKAh9avmg-frlO4fm5ASAc8IVMa56Q9nGMnQz0YSKGRoyA63RPE5MsaI7GNLeGN9OyKQBOvVeGC-rrHp_2dOLbl3daVXZbM2mU7_H1xcUZ9AXKfXWn6uYoNjr-i4-FjoPDFlpGV5YbnJWyXlKkZec51lhjeotiGBc-J0i-DNGLeFOdwtsVoQxuCrfaRs0knQHbCzZCO6C9kOe6SfpHZeDwX0n8P9Wg8xazyADh1vlqn_-HrxJrx9PH_fAuHs96vTC6iSEULs5l4co0gmTyGKNdaQiopTSMDLcaE-gXFYV1g6YqULIwbM01tEQE2mNKYnimBC0D1bSLE0O_Nip5G1qqbHEgXfCoeTYMh1LFispoyaI67ELXdmE-9MqXkStB3sWi6wJnzXRRu6CTQC_ImelVcYfYi7r9Ipvn124jv5r9OG_os_AWm806Iv-zfDuCKz7J6UO9xis5K_z5MSxjVydFtX0CTMD0tg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Stochastic+MPC+with+offline+uncertainty+sampling&rft.jtitle=Automatica+%28Oxford%29&rft.au=Lorenzen%2C+Matthias&rft.au=Dabbene%2C+Fabrizio&rft.au=Tempo%2C+Roberto&rft.au=Allg%C3%B6wer%2C+Frank&rft.date=2017-07-01&rft.issn=0005-1098&rft.volume=81&rft.spage=176&rft.epage=183&rft_id=info:doi/10.1016%2Fj.automatica.2017.03.031&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_automatica_2017_03_031 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0005-1098&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0005-1098&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0005-1098&client=summon |