14 papers · 1 filter
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-…
Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks
Shihao Li, Jiachen Li, Dongmei Chen
Data attribution methods identify which training examples are responsible for a model's predictions, but their sensitivity to distributional perturbations undermines practical reli…
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…
Algorithm-Relative Trajectory Valuation in Policy Gradient Control
Shihao Li, Jiachen Li, Jiamin Xu +3
We study how trajectory value depends on the learning algorithm in policy-gradient control. Using Trajectory Shapley in an uncertain LQR, we find a negative correlation between Per…
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…
Constrained Optimal Planning to Minimize Battery Degradation of Autonomous Mobile Robots
Jiachen Li, Jian Chu, Feiyang Zhao +3
This paper proposes an optimization framework that addresses both cycling degradation and calendar aging of batteries for autonomous mobile robot (AMR) to minimize battery degradat…
Robust Optimal Task Planning to Maximize Battery Life
Jiachen Li, Chu Jian, Feiyang Zhao +3
This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement…
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…