ArticleIET systems biology2018
Robust adaptive Lyapunov-based control of hepatitis B infection.
Article in IET systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- A novel passivity-based design for Lipschitz nonlinear systems with application to hepatitis B disease.Theory in biosciences = Theorie in den Biowissenschaften · 2025Article
- Adaptive Deformation Control for Elastic Linear Objects.Frontiers in robotics and AI · 2022Article
- Efficient prediction of drug-drug interaction using deep learning models.IET systems biology · 2020Article
- Robust control of HIV infection by antiretroviral therapy: a super-twisting sliding mode control approach.IET systems biology · 2019Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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