Minimization of constraint violation probability in model predictive control

While Robust Model Predictive Control considers the worst-case system uncertainty, Stochastic Control, using chance constraints, provides less conservative solutions by allowing a certain constraint violation probability depending on predefined risk parameter. However, for safety-critical systems it is not only important to bound but reduce this as much possible. Therefore, an approach necessary that minimizes while ensuring optimization problem remains feasible. We propose novel scheme yields solution with minimal norm in environment uncertainty. After guaranteed then also optimized respect other control objectives. Further, possible account changes over time of support first present general method and provide uncertainties symmetric, unimodal density function. Recursive feasibility convergence are proved. A simulation example demonstrates effectiveness proposed method.