Sample-efficient counterfactual tuning for compressor pressure control

In controlled industrial environments, ensuring safety and performance during controller tuning is a challenging and critical task. In particular, control loops in compressor–plenum–throttle systems cannot tolerate costly interruptions, and aggressive excitation may lead to unsafe operating regimes. Given the wide availability of historical data under safe operation, this paper introduces a counterfactual explainability approach for sample-efficient retuning of compressor control loops. The proposed data-driven algorithm determines, without an explicit plant model or previous control law, the smallest controller adjustment required to achieve predefined performance specifications while maintaining closed-loop stability. The effectiveness of the method is demonstrated through an extensive Monte Carlo simulation study, where the proposed approach computes low magnitude and interpretable adjustments in the controller space that lead to improved settling time and reduced overshoot.