Distributed model predictive control of leader-follower systems using an interior point method with efficient computations

This paper focuses on distributed model predictive control for large-scale systems comprised of interacting linear subsystems, where the necessary online computations can be distributed amongst them. A model predictive controller based on a distributed interior point method is derived, in which stabilizing control inputs can be computed distributively by every subsystem of the network. We introduce local terminal sets and costs, which together satisfy distributed invariance conditions for the whole system and guarantee stability of the closed-loop interconnected system. We show that the synthesis of both terminal sets and terminal cost functions can be done in a distributed framework.