Nonlinear adaptive control method for treatment of uncertain hepatitis B virus infection

Aghajanzadeh, Omid; Sharifi, Mojtaba; Tashakori, Shabnam; Zohoor, Hassan

doi:10.1016/j.bspc.2017.06.008

Cited by 19 publications

(12 citation statements)

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“…(19) and (21) for the Synergetic controller, by eqs. (34) and (43) for the Lyapunov Redesign controller and by the fuzzy rules for MT-FIS for treatment period of 90 days. The initial conditions at the start of the chronic stage of HCV for uninfected hepatocytes, infected hepatocytes and virions were x = [555 cells/ mL, 50, 000 cells/mL, 100, 000 cells/mL] T , respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Fuzzy logic and Lyapunov‐based non‐linear controllers for HCV infection

Hamza

Ahmad

Uneeb

2021

IET Systems Biology

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Hepatitis C is the liver disease caused by the Hepatitis C virus (HCV) which can lead to serious health problems such as liver cancer. In this research work, the non‐linear model of HCV having three state variables (uninfected hepatocytes, infected hepatocytes and virions) and two control inputs has been taken into account, and four non‐linear controllers namely non‐linear PID controller, Lyapunov Redesign controller, Synergetic controller and Fuzzy Logic‐Based controller have been proposed to control HCV infection inside the human body. The controllers have been designed for the anti‐viral therapy in order to control the amount of uninfected hepatocytes to the desired safe limit and to track the amount of infected hepatocytes and virions to their reference value which is zero. One control input is the Pegylated interferon (peg‐IFN‐α) which acts in reducing the infected hepatocytes and the other input is ribavirin which blocks the production of virions. By doing so, the uninfected hepatocytes increase and achieve the required safe limit. Lyapunov stability analysis has been used to prove the stability of the whole system. The comparative analysis of the proposed nonlinear controllers using MATLAB/Simulink have been done with each other and with linear PID. These results depict that the infected hepatocytes and virions are reduced to the desired level, enhancing the rate of sustained virologic response (SVR) and reducing the treatment period as compared with previous strategies introduced in the literature.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In the case of HCV treatment, the limitation of drug efficacy should be considered. Adaptive non‐linear controller has been proposed in [ 34 ] for the control of HBV infection. Practical limitations of treatment implementation, such as unavailability of states and efficacy limitations have not been considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic and Lyapunov‐based non‐linear controllers for HCV infection

Hamza

Ahmad

Uneeb

2021

IET Systems Biology

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“…They determined the model parameters based on clinical information of past patients. Aghajanzadeh et al [23] suggested an adaptive control strategy for hepatitis B virus infection inside the human body by antiviral drugs. They considered model parameters uncertainties on model parameters and employed adaptive controller to control the dynamic despite uncertainties of the system.…”

Section: Related Research Workmentioning

confidence: 99%

Hepatitis C Virus Epidemic Control Using a Nonlinear Adaptive Strategy

Khodaei-Mehr¹,

Tangestanizadeh²,

Sharifi³

et al. 2020

Preprint

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Hepatitis C is a viral infection that appears as a result of the Hepatitis C Virus (HCV), and it has been recognized as the main reason for liver diseases. HCV incidence is growing as an important issue in the epidemiology of infectious diseases. In the present study, a mathematical model is employed for simulating the dynamics of HCV outbreak in a population. The total population is divided into five compartments, including unaware and aware susceptible, acutely and chronically infected, and treated classes. Then, a Lyapunov-based nonlinear adaptive method is proposed for the first time to control the HCV epidemic considering modelling uncertainties. A positive definite Lyapunov candidate function is suggested, and adaptation and control laws are attained based on that. The main goal of the proposed control strategy is to decrease the population of unaware susceptible and chronically infected compartments by pursuing appropriate treatment scenarios. As a consequence of this decrease in the mentioned compartments, the population of aware susceptible individuals increases and the population of acutely infected and treated humans decreases.The Lyapunov stability theorem and Barbalat's lemma are employed in order to prove the tracking convergence to desired population reduction scenarios. Based on the acquired numerical results, the proposed nonlinear adaptive controller can achieve the above-mentioned objective by adjusting the inputs

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“…For instance, Moradi et al [32, 33] have suggested adaptive robust controllers for the cancer chemotherapy to manipulate the drug usage and consequently the tumour volume. A non‐linear adaptive control scheme has been also designed for the treatment of uncertain hepatitis B virus infection by Aghajanzadeh et al [34]. Another adaptive controller and feedback error learning has been presented by Rouhollahi et al [35] for rehabilitation therapy in Parkinson's disease.…”

Section: Introductionmentioning

confidence: 99%