  We present a hierarchical model predictive control approach for large-scale
systems based on dual decomposition. The proposed scheme allows coupling in
both dynamics and constraints between the subsystems and generates a primal
feasible solution within a finite number of iterations, using primal averaging
and a constraint tightening approach. The primal update is performed in a
distributed way and does not require exact solutions, while the dual problem
uses an approximate subgradient method. Stability of the scheme is established
using bounded suboptimality.
