68 citations · 98 across the 14 of their papers we have counts for
4 papers · 1 filter
Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem
Marco C. Campi, Simone Garatti
This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is…
Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
Dario Paccagnan, Daniel Marks, Marco C. Campi +1
Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity. In safety-critical environments, deploying these met…
A Scenario-Based Approach for Stochastic Economic Model Predictive Control with an Expected Shortfall Constraint
Alireza Arastou, Algo Carè, Ye Wang +2
This paper presents a novel approach to stochastic economic model predictive control (SEMPC) that minimizes average economic cost while satisfying an empirical expected shortfall (…
Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency (Extended Version)
Algo Carè, Erik Weyer, Balázs Cs. Csáji +1
Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence regions for the unknown system parameters. In this paper, we study SPS for ARX systems, and e…