Data-Driven Distributed Predictive Control via Network Optimization

Ahmed Allibhoy, Jorge Cortes
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:838-839, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v120-allibhoy20a, 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}, pages = {838--839}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/allibhoy20a/allibhoy20a.pdf}, url = {https://proceedings.mlr.press/v120/allibhoy20a.html}, 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.} }
Endnote
%0 Conference Paper %T Data-Driven Distributed Predictive Control via Network Optimization %A Ahmed Allibhoy %A Jorge Cortes %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-allibhoy20a %I PMLR %P 838--839 %U https://proceedings.mlr.press/v120/allibhoy20a.html %V 120 %X 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.
APA
Allibhoy, A. & Cortes, J.. (2020). Data-Driven Distributed Predictive Control via Network Optimization. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:838-839 Available from https://proceedings.mlr.press/v120/allibhoy20a.html.

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