Constrained control design via a simulation-based scenario approach

This paper deals with constrained control design for linear systems affected by stochastic disturbances. The goal is to optimize the control performance while guaranteeing that the constraints are satisfied for most of the disturbance realizations, that is with probability 1 − . In mathematical terms, this amounts to solve a “chance-constrained” optimization program and we introduce here a randomized approach to this problem that builds on certain recent results in robust convex optimization.

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