Confidence Regions for Predictions of Online Learning-Based Control. Issue 2 (2020) - Capone, Alexandre; Lederer, Armin; Hirche, Sandra
Abstract: Although machine learning techniques are increasingly employed in control tasks, few methods exist to predict the behavior of closed-loop learning-based systems. In this paper, we introduce a method for computing confidence regions of closed-loop system trajectories under an online learning-based control law. We employ a sampling-based approximation and exploit system properties to prove that the computed confidence regions are correct with high probability. In a numerical simulation, we show that the proposed approach accurately predicts correct confidence regions.