A new stochastic nonlinear model predictive control method for systems with additive noise is proposed. State distribution is approximated by Gaussian mixture with error bounds in Wasserstein distance. This yields closed-form expressions for expected costs and chance constraints, and the problem is solvable via nonlinear programming with correctness guarantees.
The method improves efficiency and robustness of control under uncertainty, relevant for managing large systems with stochastic disturbances.
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