Recursively feasible stochastic model predictive control using indirect feedback
We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constra...
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| Published in | Automatica (Oxford) Vol. 119; p. 109095 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.09.2020
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| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/j.automatica.2020.109095 |
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| Abstract | We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasible and guarantees chance constraint satisfaction for the closed-loop system, which is typically challenging for systems under unbounded disturbances. Under an i.i.d. zero-mean assumption, we provide an average asymptotic performance bound. A building control example illustrates the approach in an application with time-varying, correlated disturbances. |
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| AbstractList | We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasible and guarantees chance constraint satisfaction for the closed-loop system, which is typically challenging for systems under unbounded disturbances. Under an i.i.d. zero-mean assumption, we provide an average asymptotic performance bound. A building control example illustrates the approach in an application with time-varying, correlated disturbances. |
| ArticleNumber | 109095 |
| Author | Zeilinger, Melanie N. Wabersich, Kim P. Hewing, Lukas |
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| Cites_doi | 10.23919/ECC.2018.8550160 10.1016/j.ifacol.2018.11.010 10.1109/TAC.2016.2625048 10.1016/j.automatica.2016.03.016 10.1016/j.automatica.2008.03.027 10.1109/CDC.2013.6761117 10.1016/j.automatica.2008.06.017 10.1109/TAC.2014.2310066 10.1016/j.automatica.2003.08.009 10.1109/TAC.2010.2086553 10.1016/j.jprocont.2016.03.005 10.1109/CDC.2018.8619554 10.1109/MCS.2016.2602087 10.23919/ECC.2018.8550520 10.1016/j.automatica.2012.06.074 10.1016/j.automatica.2015.02.039 |
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| Title | Recursively feasible stochastic model predictive control using indirect feedback |
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