Convergence and H2-Property of the Discrete-Time Extended Kalman Filter Used as a Nonlinear Observer

2018 ◽  
Author(s):  
Jennifer L. Bonniwell ◽  
Susan C. Schneider ◽  
Edwin E. Yaz
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Nonlinear Observer

scholarly journals A Nonlinear Observer to Estimate the Effective Reproduction Number of Infectious Diseases

2021 ◽  
Vol 4 (1) ◽  
pp. 39-45
Author(s):  
Agus Hasan
Keyword(s):  
Kalman Filter ◽  
Infectious Diseases ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Sir Model ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Matrix Inequality

In this paper, we design a Nonlinear Observer (NLO) to estimate the effective reproduction number (Rt) of infectious diseases. The NLO is designed from a discrete-time augmented Susceptible-Infectious-Removed (SIR) model. The observer gain is obtained by solving a Linear Matrix Inequality (LMI). The method is used to estimate Rt in Jakarta using epidemiological data during COVID-19 pandemic. If the observer gain is tuned properly, this approach produces similar result compared to existing approach such as Extended Kalman filter (EKF).

scholarly journals A Nonlinear Observer to Estimate the Effective Reproduction Number of Infectious Diseases

2021 ◽  
Author(s):  
Agus Hasan
Keyword(s):  
Kalman Filter ◽  
Infectious Diseases ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Sir Model ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Matrix Inequality

AbstractIn this paper, we design a Nonlinear Observer (NLO) to estimate the effective reproduction number (ℛt) of infectious diseases. The NLO is designed from a discrete-time augmented Susceptible-Infectious-Removed (SIR) model. The observer gain is obtained by solving a Linear Matrix Inequality (LMI). The method is used to estimate ℛt in Jakarta using epidemiological data during COVID-19 pandemic. If the observer gain is tuned properly, this approach produces similar result compared to existing approach such as Extended Kalman filter (EKF).

Dynamic Sliding Mode Control of Air-to-Fuel Ratio in Internal Combustion Engines Using the Hybrid Extended Kalman Filter

2012 ◽  
Vol 433-440 ◽  
pp. 2092-2098 ◽  
Author(s):  
Majid Zohari ◽  
Mohamadreza Ahmadi ◽  
Hamed Mojallali
Keyword(s):  
Kalman Filter ◽  
Sliding Mode Control ◽  
Extended Kalman Filter ◽  
Sliding Mode ◽  
Nonlinear Observer ◽  
Superior Performance ◽  
Fuel Ratio ◽  
Mode Control ◽  
Dynamic Sliding Mode Control ◽  
Dynamic Sliding Mode

The large modeling uncertainties and the nonlinearities associated with air manifold and fuel injection in spark ignition (SI) engines has given rise to difficulties in the task of designing an adequate controller for air-to-fuel ratio (AFR) control. Although sliding mode control approaches has been suggested, the inescapable time-delay between control action and measurement update results in chattering. This paper proposes the implementation of a nonlinear observer based control scheme incorporating the hybrid extended Kalman filter (HEKF) and the dynamic sliding mode control (DSMC). The results established upon the proposed methodology are given which demonstrate superior performance in terms of reducing the chattering magnitude.

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