Feedback control of an HBV model based on ensemble kalman filter and differential evolution

Jang, Junyoung; Jang, Kyung‐In; Kwon, Hye Won; Lee, Jee Hyun

doi:10.3934/mbe.2018030

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…The use of fuzzy controllers and various optimization algorithms for disease control are also applied in the study by Sheikhan and Ghoreishi. 32 In the study by Jang, 33 feedback control based on Kalman filter and differential evolution is reported. Lyapunov-based method has been utilized in the study by Ding and Wang.…”

Section: Introductionmentioning

confidence: 99%

Virtually Euler–Lagrange form for feedback passivation of a wide range of non-Euler–Lagrange systems: Adaptive passivity-based control of hepatitis B disease

Gorji

Fakharian

Abbasi

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a novel technique of feedback passivation is developed for a wide range of non-Euler–Lagrange systems. The reason for this naming “Virtually Euler-Lagrange” is that it is adopted from the error dynamics of Euler–Lagrange systems. An adaptive passivity-based control strategy is proposed in virtually Euler–Lagrange form to treat hepatitis B virus infection. There are several remarkable differences between classic form of passivation and the proposed method, for example, specific and predefined template of storage function in virtually Euler–Lagrange form, which has positive definite auto-extracted form. So, it will be appropriate and time-saving for designing passivity-based control. On the contrary, the process of passivation and control design in conventional formulation is based on time-invariant input-affine systems; however, in virtually Euler–Lagrange form, the system can belong to a wider category. In this regard, two theorems have been proposed to expand the scope of passivity-based control design. Some control design conditions will be released as a result of using this new method. Furthermore, for the first time, passivity-based control has been implemented in the biological area. Control profile design, simulation results, and sensitivity analysis are carried out to evaluate capability of this control approach. Some main biological limitations are also considered, which make the proposed control more realistic.

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Section: Introductionmentioning

confidence: 99%

Virtually Euler–Lagrange form for feedback passivation of a wide range of non-Euler–Lagrange systems: Adaptive passivity-based control of hepatitis B disease

Gorji

Fakharian

Abbasi

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…As a variant of the Kalman filter, the Ensemble Kalman filter (EnKF) [3] is introduced to deal with the nonlinear state-space model, assuming that all probability distributions are Gaussian. There have been numerous applications involving EnKF, such as physical models of the atmosphere, oceans in geophysical systems [4,5], and biological science [6].…”

Section: Introductionmentioning

confidence: 99%

Particle network EnKF for large-scale data assimilation

Lu²

2022

Front. Phys.

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The Ensemble Kalman filter (EnKF) is a classic method of data assimilation. For distributed sampling, the conventional EnKF usually requires a centralized server to integrate the predictions of all particles or a fully-connected communication network, causing traffic jams and low bandwidth utilization in high-performance computing. In this paper, we propose a novel distributed scheme of EnKF based on network setting of sampling, called Particle Network EnKF. Without a central server, every sampling particle communicates with its neighbors over a sparsely connected network. Unlike the existing work, this method focuses on the distribution of sampling particles instead of sensors and has been proved effective and robust on numerous tasks. The numerical experiments on the Lorenz-63 and Lorenz-96 systems indicate that, with proper communication rounds, even on a sparse particle network, this method achieves a comparable performance to the standard EnKF. A detailed analysis of effects of the network topology and communication rounds is performed. Another experiment demonstrating a trade-off between the particle homogeneity and performance is also provided. The experiments on the whole-brain neuronal network model show promises for applications in large-scale assimilation problems.

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“…The Ensemble Kalman Filter (EnKF) is a Bayesian filtering algorithm used to estimate unknown states and parameters of nonlinear systems by combining model predictions with available system observations [1][2][3]. While these algorithms are commonly used for data assimilation in applications to weather prediction [4][5][6] and guidance, navigation, and control [7][8][9], ensemble Kalman-type filters have recently been utilized for parameter estimation and forecast prediction in a variety of epidemiological studies [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effects of Observation Function Selection in Ensemble Kalman Filtering for Epidemic Models

Mitchell,

Arnold

2020

Preprint

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The Ensemble Kalman Filter (EnKF) is a Bayesian filtering algorithm utilized in estimating unknown model states and parameters for nonlinear systems. An important component of the EnKF is the observation function, which connects the unknown system variables with the observed data. These functions take different forms based on modeling assumptions with respect to the available data and relevant system parameters. The goal of this research is to analyze the effects of observation function selection in the EnKF in the setting of epidemic modeling, where a variety of observation functions are used in the literature. In particular, four observation functions of different forms and various levels of complexity are examined in connection with the classic Susceptible-Infectious-Recovered (SIR) model. Results demonstrate the importance of choosing an observation function that well interprets the available data on the corresponding EnKF estimates in several filtering scenarios, including state estimation with known parameters, and combined state and parameter estimation with both constant and time-varying parameters. Numerical experiments further illustrate how modifying the observation noise covariance matrix in the filter can help to account for uncertainty in the observation function in certain cases.

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Feedback control of an HBV model based on ensemble kalman filter and differential evolution

Cited by 10 publications

References 18 publications

Virtually Euler–Lagrange form for feedback passivation of a wide range of non-Euler–Lagrange systems: Adaptive passivity-based control of hepatitis B disease

Virtually Euler–Lagrange form for feedback passivation of a wide range of non-Euler–Lagrange systems: Adaptive passivity-based control of hepatitis B disease

Particle network EnKF for large-scale data assimilation

Analyzing the Effects of Observation Function Selection in Ensemble Kalman Filtering for Epidemic Models

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