Admissible sets for chance-constrained difference inclusions

We study the Maximal Admissible Set (MAS) of a linear difference inclusion under a chance constraint involving random variables with bounded support. After showing that it is difficult to compute this set in general, we suggest inner and outer approximations that can be computed using existing algorithms. The inner approximations are themselves constraint admissible sets and can be represented by a finite number of chance constraints. We give examples to demonstrate the low level of conservatism of these approximations and to illustrate the potential application in Model Predictive Control.

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