Network-Realised Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition

TL;DR

This paper introduces a scalable two-layer control architecture utilizing network-realized model predictive control with explicit closed-loop expressions and distributed implementation for constraint management.

Contribution

It presents a novel two-layer control scheme with explicit closed-loop maps and an offline design method for scalable, distributed predictive control.

Findings

Derived explicit closed-loop maps for the control scheme.

Proposed an offline model-matching design procedure.

Enabled distributed state-space implementation for predictive control.

Abstract

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.