A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback

This paper deals with model predictive control of uncertain linear discrete-time systems with polytopic constraints on the input and chance constraints on the states. When having polytopic constraints and bounded disturbances, the robust problem with an open-loop prediction formulation is known to b...

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Bibliographic Details
Published in2008 47th IEEE Conference on Decision and Control pp. 4731 - 4736
Main Authors Oldewurtel, F., Jones, C.N., Morari, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Automatic control
Control systems
Linear feedback control systems
Open loop systems
Predictive control
Predictive models
Robustness
State feedback
Stochastic processes
Stochastic systems
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ISBN9781424431236
1424431239
ISSN0191-2216
DOI10.1109/CDC.2008.4738806

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Summary:This paper deals with model predictive control of uncertain linear discrete-time systems with polytopic constraints on the input and chance constraints on the states. When having polytopic constraints and bounded disturbances, the robust problem with an open-loop prediction formulation is known to be conservative. Recently, a tractable closed-loop prediction formulation was introduced, which can reduce the conservatism of the robust problem. We show that in the presence of chance constraints and stochastic disturbances, this closed-loop formulation can be used together with a tractable approximation of the chance constraints to further increase the performance while satisfying the chance constraints with the predefined probability.
ISBN:9781424431236
1424431239
ISSN:0191-2216
DOI:10.1109/CDC.2008.4738806