We consider the Chance Constrained Model Predictive Control problem for polynomial systems subject to
disturbances. In this problem, we aim at finding optimal control input for given disturbed dynamical system to
minimize a given cost function subject to probabilistic constraints, over a finite horizon. The control laws provided
have a predefined (low) risk of not reaching the desired target set. Building on the theory of measures and moments,
a sequence of finite semidefinite programmings are provided, whose solution is shown to converge to the optimal
solution of the original problem. Numerical examples are presented to illustrate the computational performance of
the proposed approach. Problem Formulation Numerical ResultsPaper |

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