A robust distributed model predictive control algorithm

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method.

► An on-line algorithm for distributed model predictive control strategy that explicitly considers model errors is proposed.
► The key idea is to decompose the model of the whole system into N subsystems and then obtain a local state feedback controller by minimizing an upper bound on a robust performance objective for each subsystem.
► The method is also suitable for achieving other control objectives such as Nash equilibrium or decentralized control.
► It was proven that if the algorithm is terminated at any feasible intermediate iteration the robust stability is still maintained and after a sufficient number of iterations, similar performance to centralized control can be obtained.
► In some cases when the algorithm is terminated before reaching convergence it can provide lower computation time compared to centralized MPC while the loss in performance is not significant.