Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
This addresses the need for rigorous safety guarantees in safety-critical systems and control environments, offering a novel approach that is not incremental but provides broad applicability.
The paper tackles the problem of ensuring safety and performance guarantees in data-driven control methods without sacrificing data for testing or constraining algorithm choice, resulting in a framework that outperforms existing methods across various control applications.
Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity. In safety-critical environments, deploying these methods requires rigorous guarantees, a need that has motivated much recent work at the interface of statistical learning and control. However, many existing approaches achieve this goal at the cost of sacrificing valuable data for testing and calibration, or by constraining the choice of learning algorithm, thus leading to suboptimal performances. In this paper, we describe Pick-to-Learn (P2L) for Systems and Control, a framework that allows any data-driven control method to be equipped with state-of-the-art safety and performance guarantees. P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes. In presenting a comprehensive version of P2L for systems and control, this paper demonstrates its effectiveness across a range of core problems, including optimal control, reachability analysis, safe synthesis, and robust control. In many of these applications, P2L delivers designs and certificates that outperform commonly employed methods, and shows strong potential for broad applicability in diverse practical settings.