5 papers
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…
An Autocovariance Least-Squares-Based Data-Driven Kalman Filter for Unknown Systems
Suyang Hu, Xiaoxu Lyu, Peihu Duan +2
This article investigates the problem of data-driven state estimation for linear systems with both unknown system dynamics and noise covariances. We propose an Autocovariance Least…
On the Effects of Modeling Errors on Distributed Continuous-time Filtering
Xiaoxu Lyu, Shilei Li, Dawei Shi +1
This paper offers a comprehensive performance analysis of the distributed continuous-time filtering in the presence of modeling errors. First, we introduce two performance indices,…