Distributed model predictive control based on decomposition-coordination and networking
Résumé
This paper is devoted to distributed nonlinear model predictive control (MPC) design in discrete time through the use of both an augmented Lagrangian formulation and price-decomposition-coordination. We show how Lagrangian relaxation can be used to design a distributed MPC scheme, which allows dramatic reduction of the computational requirements and is well suited for networked control applications. The effectiveness of this approach is demonstrated for the so-called Load Frequency Control of a two-area power system in presence of communication constraints.