5 papers · 1 filter
Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control
Min Kim, José Leonardo Brenes, Fred Hadaegh +1
We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects classical disturbance-accommod…
Geometric Fault Identification via Mirror Descent Learning
Mahdi Taheri, Haeyoon Han, Soon-Jo Chung +1
This paper develops a fault detection and identification (FDI) method for nonlinear control-affine systems under simultaneous actuator and sensor faults. We adopt a geometric appro…
Perron-Frobenius Contractive Operator Matching for Data-Driven Reachable Fault Identification and Recovery
Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung +1
This paper focuses on data-driven fault detection, identification, and recovery (FDIR) for nonlinear control-affine systems under actuator faults. We create a unified framework in…
Data-Driven Probabilistic Fault Detection and Identification via Density Flow Matching
Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung +1
Fault detection and identification (FDI) is critical for maintaining the safety and reliability of systems subject to actuator and sensor faults. In this paper, the problem of FDI…
Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control
Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh
This paper studies the problem of real-time fault recovery control for nonlinear control-affine systems subject to actuator loss of effectiveness faults and external disturbances.…