{"ID":2839940,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14280","arxiv_id":"2511.14280","title":"A graph-informed regret metric for optimal distributed control","abstract":"We consider the optimal control of large-scale systems using distributed controllers with a network topology that mirrors the coupling graph between subsystems. In this work, we introduce spatial regret, a graph-informed metric that measures the worst-case performance gap between a distributed controller and an oracle which is assumed to have access to additional sensor information. The oracle's graph is a user-specified augmentation of the available information graph, resulting in a benchmark policy that highlights disturbances for which additional sensor information would significantly improve performance. Minimizing spatial regret yields distributed controllers-respecting the nominal information graph-that emulate the oracle's response to disturbances that are characteristic of large-scale networks, such as localized perturbations. We show that minimizing spatial regret admits a convex reformulation as an infinite program with a finite-dimensional approximation. To scale to large networks, we derive a computable upper bound on the spatial regret metric whose minimization problem can be solved in a distributed way. Numerical experiments on power-system models demonstrate that the resulting controllers mitigate localized disturbances more effectively than controllers optimized using classical metrics.","short_abstract":"We consider the optimal control of large-scale systems using distributed controllers with a network topology that mirrors the coupling graph between subsystems. In this work, we introduce spatial regret, a graph-informed metric that measures the worst-case performance gap between a distributed controller and an oracle...","url_abs":"https://arxiv.org/abs/2511.14280","url_pdf":"https://arxiv.org/pdf/2511.14280v1","authors":"[\"Daniele Martinelli\",\"Andrea Martin\",\"Giancarlo Ferrari-Trecate\",\"Luca Furieri\"]","published":"2025-11-18T09:14:21Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"math.OC\"]","methods":"[]","has_code":false}