7 papers
Hierarchical Variational Kalman Filtering
Shilei Li, Dawei Shi, Wei Zheng +1
Traditional variational Kalman filtering with unknown noise statistics suffers from inconsistent process covariance estimation and slow convergence speed, limiting its practical ut…
Data-Driven Co-Design of Event-Triggered and Sparse Control for Resource-Aware Networked Control Systems
Zhaohua Yang, Xiaoxu Lyu, Dawei Shi +1
This paper investigates the data-driven co-design of event-triggered control (ETC) and sparse control (SC) for networked control systems (NCSs) with unknown linear dynamics. While…
Variational Robust Kalman Filters: A Unified Framework
Shilei Li, Dawei Shi, Hao Yu +1
Robustness and adaptivity are two competing objectives in Kalman filters (KF). Robustness involves temporarily inflating prior estimates of noise covariances, while adaptivity upda…
Online Coreset Selection for Learning Dynamic Systems
Jingyuan Li, Dawei Shi, Ling Shi
With the increasing availability of streaming data in dynamic systems, a critical challenge in data-driven modeling for control is how to efficiently select informative data to cha…
Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems
Kaikai Zheng, Dawei Shi, Yang Shi +1
Thompson sampling (TS) is a Bayesian randomized exploration strategy that samples options (e.g., system parameters or control laws) from the current posterior and then applies the…
Open-/Closed-loop Active Learning for Data-driven Predictive Control
Shilun Feng, Dawei Shi, Yang Shi +1
An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear syste…