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A Tractable Approximation of Chance Constrained Stochastic MPC based on Affine Disturbance Feedback


F. Oldewurtel

Conference on Decision and Control (CDC), Cancun, Mexico

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.


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