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

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3 Author(s)
Ion Necoara ; Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SCD, Kasteelpark Arenberg 10, B-3001 (Heverlee), Belgium ; Dang Doan ; Johan A. K. Suykens

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 (L. Necoara et al., 2008) 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.

Published in:

Decision and Control, 2008. CDC 2008. 47th IEEE Conference on

Date of Conference:

9-11 Dec. 2008