Fuzzy Logic Approach for Solving an Optimal Control Problem of an Uninfected Hepatitis B Virus Dynamics

Ntaganda, Jean Marie; Gahamanyi, Marcel

doi:10.4236/am.2015.69136

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Some methods in multiplicity of infection are currently employed for quantification of viruses and infected cells per unit volume of liquids [24]. Consequently, similar to the previous studies on the modelling [2, 25] and control [7, 15, 16, 26] of the HBV infection, it is considered that the viruses and cells measurements are possible for implementation of the proposed strategy. Also, the non‐linear HBV model originated from [2] in which the state variables ( x , y and v ) were measured to obtain the dynamic structure (1)–(3) and identify its parameters.…”

Section: Robust Adaptive Controller Designmentioning

confidence: 99%

“…In particular, some control strategies have been applied for the HBV treatment. For instance, Ntaganda and Gahamanyi [ 15 ] employed a fuzzy logic for optimal control of the HBV. Su and Sun [ 7 ] compared the HBV treatment process using two different therapies (traditional Chinese and Western medicines) by employing an optimal method.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the proposed controller has the following characteristics in comparison with the previously suggested controllers for the HBV [ 7 , 15 , 16 ]: (i) being robust to the parametric uncertainties of the process using the adaptive control theory [ 18 ], and (ii) robustness against bounded unstructured modelling (non‐parametric) uncertainties and/or disturbances in the process by employing the sliding‐mode control theory [ 19 ]. Accordingly, in this paper, by combining the non‐linear adaptive control [ 18 ] and sliding‐mode control [ 19 ], a new robust adaptive Lyapunov‐based controller is presented for the HBV infection.…”

Section: Introductionmentioning

confidence: 99%

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Robust adaptive Lyapunov‐based control of hepatitis B infection

et al. 2018

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A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non-linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection. The proposed controller ensures the stability and robust performance in the presence of parametric and non-parametric uncertainties (and/or bounded disturbances). The global stability and tracking convergence of the process are investigated by employing the Lyapunov theorem. The performance of the proposed controller is evaluated using simulations by considering different levels of uncertainties. Based on the obtained results, the proposed strategy can achieve its desired objectives with different cases of uncertainties.

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Section: Robust Adaptive Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust adaptive Lyapunov‐based control of hepatitis B infection

et al. 2018

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“…26 In the field of control, in some articles, Pontryagin's maximum principle has been used to specify optimal control. [27][28][29][30] In the study by Ntaganda and Gahamany, 31 an optimal control is designed by fuzzy logic to treat the disease. The use of fuzzy controllers and various optimization algorithms for disease control are also applied in the study by Sheikhan and Ghoreishi.…”

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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“…Disease analysis is a crucial element in the field of medicine and healthcare. An inappropriate analysis of a disease often results in improper treatment that leads to complications of the ailment and eventually to death [1]. What are the major signs and symptoms of the disease and its extent or degree of symptoms on the organs?…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System

Ahmad

Khan

Abbas

et al. 2019

Journal of Healthcare Engineering

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In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.

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Fuzzy Logic Approach for Solving an Optimal Control Problem of an Uninfected Hepatitis B Virus Dynamics

Cited by 9 publications

References 22 publications

Robust adaptive Lyapunov‐based control of hepatitis B infection

Robust adaptive Lyapunov‐based control of hepatitis B infection

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

Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System

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