Application of the proximal center decomposition method to distributed model predictive control

In this paper we present a dual-based decomposition method, called here the proximal center method, to solve distributed model predictive control (MPC) problems for coupled dynamical systems but with decoupled cost and constraints. We show that the centralized MPC problem can be recast as a separable convex problem for which our method can be applied. In [9] we have provided convergence proofs and efficiency estimates for the proximal center method which improves with one order of magnitude the bounds on the number of iterations of the classical dual subgradient method. The new method is suitable for application to distributed MPC since it is highly parallelizable, each subsystem uses local information and the coordination between the local MPC controllers is performed via the Lagrange multipliers corresponding to the coupled dynamics. Simulation results are also included.