Data-based Receding Horizon Control of Linear Network Systems
Optimization and Control
2020-12-02 v2 Multiagent Systems
Systems and Control
Systems and Control
Abstract
We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, relying instead on learning a data-based representation from a single sample trajectory. We employ this representation to reformulate the finite-horizon Linear Quadratic Regulator problem as a network optimization with separable objective functions and locally expressible constraints. We show that the controller resulting from approximately solving this problem using a distributed optimization algorithm in a receding horizon manner is stabilizing. We validate our results through numerical simulations.
Cite
@article{arxiv.2003.09813,
title = {Data-based Receding Horizon Control of Linear Network Systems},
author = {Ahmed Allibhoy and Jorge Cortés},
journal= {arXiv preprint arXiv:2003.09813},
year = {2020}
}