scholarly journals Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

2017 ◽  
Author(s):  
Alexandru Popovici ◽  
Peter Zaal ◽  
Daan M. Pool
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Manual Control ◽  
Time Varying ◽  
Control Behavior

scholarly journals A Compartmental Epidemic Model Incorporating Probable Cases to Model COVID-19 Outbreak in Regions with Limited Testing Capacity

2020 ◽  
Author(s):  
Agus Hasan ◽  
Yuki Nasution
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Epidemic Model ◽  
Laboratory Testing ◽  
Reproduction Number ◽  
Well Posedness ◽  
Time Varying ◽  
Policy Makers ◽  
Limited Testing ◽  
The Basic Reproduction Number

We propose a new compartmental epidemic model taking into account people who has symptoms with no confirmatory laboratory testing (probable cases). We prove well-posedness of the model and provide an explicit expression for the basic reproduction number R0. We use the model together with an extended Kalman filter (EKF) to estimate the time-varying effective reproduction number Rt of COVID-19 in West Java province, Indonesia, where laboratory testing capacity is limited. Based on our estimation, the value of Rt is higher when the probable cases are taken into account. This correction can be used by decision and policy makers when considering re-opening policy and evaluation of measures.

scholarly journals Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

2021 ◽  
Author(s):  
Xiaoxiong Zhang ◽  
Jia He ◽  
Xugang Hua ◽  
Zhengqing Chen ◽  
Ou Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Structural Parameters ◽  
Optimization Procedure ◽  
Unbiased Estimation ◽  
Time Varying ◽  
Time Step ◽  
Varying Parameters ◽  
Unknown Inputs ◽  
Time Varying Parameters

Abstract To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

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