Optimal partitioning in distributed model predictive control

In this paper we develop the algorithms for optimal partitioning of a distributed control system into subsystems of manageable size for which control actions are found using model predictive control (MPC) technology. We will first define a realization-invariant weighting matrix to represent the distributed system as a directed graph. We then develop a formulation in which an open loop performance metric is used to partition the distributed system into subsystem in which local MPC problems will be solved. This partitioning however is balanced against the closed loop cost of the control actions for the overall distributed system. Effective algorithms for the distributed control of the large-scale systems are then proposed. Future work will include the study of the effect of the constraints in the partitioning, and the development of efficient problem formulations aimed at improving numerical properties of the proposed control algorithms.