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 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.

Published in:
Proceedings of the 47th IEEE Conference on Decision and Control, 4731-4736
Presented at:
47th IEEE Conference on Decision and Control, Cancun, Mexico, 9-11 December 2008

Note: The status of this file is: EPFL only

 Record created 2011-10-24, last modified 2018-01-28

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