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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Bibliographic Details
Published inAutomatica (Oxford) Vol. 119; p. 109095
Main Authors Hewing, Lukas, Wabersich, Kim P., Zeilinger, Melanie N.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.09.2020
Subjects
Chance constraints
Predictive control
Stochastic model predictive control
Chance constraints
Stochastic model predictive control
Predictive control
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Summary: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.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2020.109095