Jorge Cortés


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


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.


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

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