  Standard model predictive control strategies imply the online computation of
control inputs at each sampling instance, which traditionally limits this type
of control scheme to systems with slow dynamics. This paper focuses on
distributed model predictive control for large-scale systems comprised of
interacting linear subsystems, where the online computations required for the
control input can be distributed amongst them. A model predictive controller
based on a distributed interior point method is derived, for which every
subsystem in the network can compute stabilizing control inputs using
distributed computations. We introduce local terminal sets and cost functions,
which together satisfy distributed invariance conditions for the whole system,
that guarantees 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.
