Distributed Model Predictive Control with suboptimality and stability guarantees

Theory for Distributed Model Predictive Control (DMPC) is developed based on dual decomposition of the convex optimization problem that is solved in each time sample. The process to be controlled is an interconnection of several subsystems, where each subsystem corresponds to a node in a graph. We present a stopping criterion for the DMPC scheme that can be locally verified by each node and that guarantees closed loop suboptimality above a pre-specified level and asymptotic stability of the interconnected system.