14 papers · 1 filter
Data-Attributed Adaptive Control Barrier Functions: Safety-Certified Training Data Curation via Influence Analysis
Jiachen Li, Shihao Li, Dongmei Chen
Learning-based adaptation of Control Barrier Function (CBF) parameters offers a promising path toward safe autonomous navigation that balances conservatism with performance. Yet th…
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…
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…
Curriculum-Based Soft Actor-Critic for Multi-Section R2R Tension Control
Shihao Li, Jiachen Li, Christopher Martin +3
Precise tension control in roll-to-roll (R2R) manufacturing is difficult under varying operating conditions and process uncertainty. This paper presents a curriculum-based Soft Act…
Adaptive Trajectory Bundle Method for Roll-to-Roll Manufacturing Systems
Jiachen Li, Shihao Li, Christopher Martin +2
Roll-to-roll (R2R) manufacturing requires precise tension and velocity control under operational constraints. Model predictive control demands gradient computation, while sampling-…
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…
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…
From Natural Language to Certified H-infinity Controllers: Integrating LLM Agents with LMI-Based Synthesis
Shihao Li, Jiachen Li, Jiamin Xu +1
We present \textsc{S2C} (Specification-to-Certified-Controller), a multi-agent framework that maps natural-language requirements to certified state-feedback co…
An LLM-Assisted Multi-Agent Control Framework for Roll-to-Roll Manufacturing Systems
Jiachen Li, Shihao Li, Christopher Martin +3
Roll-to-roll manufacturing requires precise tension and velocity control to ensure product quality, yet controller commissioning and adaptation remain time-intensive processes depe…
RDS-DeePC: Robust Data Selection for Data-Enabled Predictive Control via Sensitivity Score
Jiachen Li, Shihao Li, Jian Chu +1
Data Enabled Predictive Control (DeePC) is an established model free approach to predictive control, but it faces two open challenges: computational complexity that scales cubicall…
AURORA: Autonomous Updating of ROM and Controller via Recursive Adaptation
Jiachen Li, Shihao Li, Dongmei Chen
Real time model based control of high dimensional nonlinear systems presents severe computational challenges. Conventional reduced order model control relies heavily on expert tuni…
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…
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…
Smart Predict-Then-Control: Control-Aware Surrogate Refinement for System Identification
Jiachen Li, Shihao Li, Dongmei Chen
This paper introduces Smart Predict Then Control (SPC), a control aware refinement procedure for model based control. SPC refines a prediction oriented model by optimizing a surrog…