A two-layer control architecture is proposed to enable scalable implementations for constraint-based decision strategies, such as model predictive controllers. The bottom layer is based upon a distributed feedback-feedforward scheme that directs the controlled network's information flow according to a pre-specified communication infrastructure. Explicit expressions for the resulting closed-loop maps are obtained, and an offline model-matching procedure is proposed for designing the first layer. The obtained control laws are deployed via distributed state-space-based implementations, and the resulting closed-loop models enable predictive control design for the constraint management procedure described in our companion paper.
@article{arxiv.2502.13042,
title = {Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition},
author = {Andrei Sperilă and Alessio Iovine and Sorin Olaru and Patrick Panciatici},
journal= {arXiv preprint arXiv:2502.13042},
year = {2026}
}