Robust Data-Driven Control
Designing uncertainty models and robust control methods directly to make data-driven control reliable.
A central challenge in data-driven control is ensuring that controllers designed from noisy, finite data are reliable when deployed on real systems. Our research addresses a key question: how should uncertainty be characterized in data-driven nonparametric models, and how should this uncertainty be used to guarantee robust performance and safety of the controlled systems?
We have developed distributionally robust formulations of data-enabled predictive control that provide probabilistic guarantees on constraint satisfaction. We have also developed methods for behavioral uncertainty quantification which are used to develop reliable control strategies directly from data.