10 papers
A graph-informed regret metric for optimal distributed control
Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate +1
We consider the optimal control of large-scale systems using distributed controllers whose network topology mirrors the coupling graph between subsystems. In this work, we introduc…
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
Andrea Martin, Ian R. Manchester, Luca Furieri
The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems. In this paper, we consider th…
Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
John Cao, Luca Furieri
We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a…
Characterizing all locally exponentially stabilizing controllers as a linear feedback plus learnable nonlinear Youla dynamics
Luca Furieri
We derive a state-space characterization of all dynamic state-feedback controllers that make an equilibrium of a nonlinear input-affine continuous-time system locally exponentially…
Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret
Vaibhav Gupta, Daniele Martinelli, Giancarlo Ferrari-Trecate +2
In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret pro…
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Junan Lin, Paul J. Goulart, Luca Furieri
The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of p…