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

Aghajanzadeh, O ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.bspc.2017.06.008
  3. Abstract:
  4. In this paper, a nonlinear adaptive control method is presented for the treatment of the Hepatitis B Virus (HBV) infection. Nonlinear dynamics of the HBV, modeling uncertainties and three state variables (the numbers of uninfected and infected cells and free viruses) are taken into account. The proposed control law is designed for the antiviral drug input such that the number of free viruses and consequently the number of infected cells decrease to the desired values. An adaptation law is also presented to overcome modeling uncertainties by updating estimations of the system parameters during the treatment period. The stability of the process and convergence to desired state values are investigated by utilizing the Lyapunov theorem. The performance of the proposed adaptive control strategy is evaluated via comprehensive simulations employing the nonlinear HBV model with different levels of uncertainty. The consideration of modeling uncertainties is in accordance with the reality where the HBV has different characteristics in different bodies. According to the obtained results, the proposed strategy can achieve the desired control objectives (reduction of viruses and infected cells) by adjusting the drug usage. © 2017 Elsevier Ltd
  5. Keywords:
  6. Antiviral drug usage ; Hepatitis B virus (HBV) infection ; Adaptive control systems ; Control theory ; Information dissemination ; Viruses ; Adaptive control strategy ; Anti-viral drugs ; Control objectives ; Hepatitis B virus infection ; Lyapunov stability ; Model uncertainties ; Non-linear adaptive controls ; Uncertainty analysis ; Antivirus agent ; Antiviral therapy ; Controlled study ; Drug use ; Hepatitis B ; Hepatitis B virus ; Mathematical model ; Nonhuman ; Nonlinear adaptive control ; Nonlinear system ; Priority journal ; Simulation
  7. Source: Biomedical Signal Processing and Control ; Volume 38 , 2017 , Pages 174-181 ; 17468094 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S1746809417301106