A Distributed Optimization-Based Approach for Hierarchical Model Predictive Control of Large-Scale Systems With Coupled Dynamics and Constraints: Extended Report
The authors 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 sub-gradient method. Stability of the scheme is established using bounded sub-optimality.