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eess.SY2026

IF-CPS: Influence Functions for Cyber-Physical Systems -- A Unified Framework for Diagnosis, Curation, and Safety Attribution

Jiachen Li, Shihao Li, Soovadeep Bakshi +2

Neural network controllers trained via behavior cloning are increasingly deployed in cyber-physical systems (CPS), yet practitioners lack tools to trace controller failures back to…

eess.SY2026

Stochastic Trajectory Influence Functions for LQR: Joint Sensitivity Through Dynamics and Noise Covariance

Jiachen Li, Shihao Li, Soovadeep Bakshi +2

We present a three-level influence hierarchy for data valuation in stochastic LQR. At the \emph{model level}, the trajectory influence surrogate $\IFm_k := H^{-1}g_k$ approximates…

eess.SY2025

DM-MPPI: Datamodel for Efficient and Safe Model Path Integral Control

Jiachen Li, Xu Duan, Shihao Li +2

We extend the Datamodels framework from supervised learning to Model Predictive Path Integral (MPPI) control. Whereas Datamodels estimate sample influence via regression on a fixed…

eess.SY2025

Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control

Jiachen Li, Shihao Li, Jiamin Xu +2

Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has…

eess.SY2025

MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control

Shihao Li, Jiachen Li, Christopher Martin +2

This paper presents a Model-Inspired Distributionally Robust Data-enabled Predictive Control (MDR-DeePC) framework for systems with partially known and uncertain dynamics. The prop…

eess.SY2025

Influence Functions for Data Attribution in Linear System Identification and LQR Control

Jiachen Li, Shihao Li, Soovadeep Bakshi +2

When a controller is designed from an identified model, its performance ultimately depends on the trajectories used for identification, but pinpointing which ones help or hurt rema…