Data-Driven Distributed Predictive Control via Network Optimization
Abstract
We consider a networked linear system where system matrices are unknown to the individual agents but sampled data is available to them. We propose a data-driven method for designing a distributed linear-quadratic controller where agents learn a non-parametric system model from a single sample trajectory in which nodes can predict future trajectories using only data available to themselves and their neighbors. Based on this system representation, we propose a control scheme where a network optimization problem is solved in a receding horizon manner. We show that the proposed control scheme is stabilizing and validate our results through numerical experiments.
Cite
Text
Allibhoy and Cortes. "Data-Driven Distributed Predictive Control via Network Optimization." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Allibhoy and Cortes. "Data-Driven Distributed Predictive Control via Network Optimization." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/allibhoy2020l4dc-datadriven/)BibTeX
@inproceedings{allibhoy2020l4dc-datadriven,
title = {{Data-Driven Distributed Predictive Control via Network Optimization}},
author = {Allibhoy, Ahmed and Cortes, Jorge},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {838-839},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/allibhoy2020l4dc-datadriven/}
}