Distributed model predictive control based on decomposition-coordination and networking

This paper is devoted to distributed nonlinear model predictive control (MPC) design for interconnected systems 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.