Robust Data-Driven Control with Safety Constraints and Fault Detection

TY - JOUR

T1 - Robust Data-Driven Control with Safety Constraints and Fault Detection

AU - Zhou, Lingan

AU - Liu, Wenjie

AU - Li, Yifei

AU - Sun, Jian

AU - Wang, Gang

AU - Xie, Lihua

N1 - Publisher Copyright: © 1963-2012 IEEE.

PY - 2026/6/1

Y1 - 2026/6/1

N2 - Direct data-driven control has emerged as a powerful alternative to traditional state-space model-based methods by directly learning control policies from data. Nevertheless, ensuring that control actions adhere to specific physical and safety constraints, a challenge often overlooked in existing direct data-driven approaches focused primarily on stability and robustness, is crucial for real-world deployment. This article addresses this gap by designing a robust data-driven state feedback control law that enforces safety-critical state constraints. Utilizing robust control techniques, we transform pointwise-in-time state constraints into reachable set constraints and formulate a semidefinite program (SDP) based on noisy input-state measurements. In addition, recognizing that various system variations and environmental disturbances can be modeled as process noise, we propose to maximize the noise tolerance of the system through a data-driven SDP, thereby enhancing resilience against operational uncertainties. To further ensure operational safety, we develop a state estimator-based fault detector that leverages noisy closed-loop state data to trigger alarms during abnormal system operation. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed safe data-driven control approach.

AB - Direct data-driven control has emerged as a powerful alternative to traditional state-space model-based methods by directly learning control policies from data. Nevertheless, ensuring that control actions adhere to specific physical and safety constraints, a challenge often overlooked in existing direct data-driven approaches focused primarily on stability and robustness, is crucial for real-world deployment. This article addresses this gap by designing a robust data-driven state feedback control law that enforces safety-critical state constraints. Utilizing robust control techniques, we transform pointwise-in-time state constraints into reachable set constraints and formulate a semidefinite program (SDP) based on noisy input-state measurements. In addition, recognizing that various system variations and environmental disturbances can be modeled as process noise, we propose to maximize the noise tolerance of the system through a data-driven SDP, thereby enhancing resilience against operational uncertainties. To further ensure operational safety, we develop a state estimator-based fault detector that leverages noisy closed-loop state data to trigger alarms during abnormal system operation. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed safe data-driven control approach.

KW - Data-driven control

KW - fault detection

KW - safety-critical control

UR - https://www.scopus.com/pages/publications/105025959850

U2 - 10.1109/TAC.2025.3647571

DO - 10.1109/TAC.2025.3647571

M3 - Article

AN - SCOPUS:105025959850

SN - 0018-9286

VL - 71

SP - 4043

EP - 4050

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

IS - 6

ER -