Jorge Cortés

Professor





Data-driven distributed predictive control via network optimization
A. Allibhoy, J. Cortés
Conference on Learning for Dynamics and Control, Berkeley, California, 2020, Proceedings of Machine Learning Research, volume 120, pp. 838-839


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.



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Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

Ph: 1-858-822-7930
Fax: 1-858-822-3107

cortes at ucsd.edu
Skype id: jorgilliyo