A distributed optimization-based approach for hierarchical model predictive control of large-scale systems with coupled dynamics and constraints
Abstract
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.
Cite
@article{arxiv.1109.1214,
title = {A distributed optimization-based approach for hierarchical model predictive control of large-scale systems with coupled dynamics and constraints},
author = {Minh Dang Doan and Tamás Keviczky and Bart De Schutter},
journal= {arXiv preprint arXiv:1109.1214},
year = {2011}
}
Comments
This is the extended version of our paper at the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, Florida, Dec. 2011. In this version the proofs are provided