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…
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…
Bias-VarianceTrade-off in Kalman Filter-Based Disturbance Observers
Shilei Li, Dawei Shi, Xiaoxu Lyu +2
The performance of disturbance observers is strongly influenced by the level of prior knowledge about the disturbance model. The simultaneous input and state estimation (SISE) algo…
Event-triggered Dual Gradient Tracking for Distributed Resource Allocation
Xiayan Xu, Xiaomeng Chen, Dawei Shi +1
High communication costs create a major bottleneck for distributed resource allocation over unbalanced directed networks. Conventional dual gradient tracking methods, while effecti…
Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons
Shilei Li, Dawei Shi, Makoto Iwasaki +3
The nominal performance of mechanical systems is often degraded by unknown disturbances. A two-degree-of-freedom control structure can decouple nominal performance from disturbance…