Distributed Distributionally Robust Model Predictive Control

Distributed stochastic model predictive control (DSMPC) of linear systems with coupled chance constraints under disturbances is investigated in this paper. We consider a practical scenario where only the mean and covariance, but not the exact distribution, of the disturbance is available. A frozen technique is utilized to ensure the satisfaction of the coupled constraints, and a deterministic convex tight reformulation is used for handling the chance constraints based on the available information of the disturbance. Recursive feasibility and convergence of the proposed method are proved. Numerical simulations are given to demonstrate the effectiveness of the proposed algorithm.