Robust adaptive Lyapunov‐based control of hepatitis B infection

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.

Adaptive Deformation Control for Elastic Linear Objects

“…These terms allow us to track a desired trajectory of the state, instead of just reaching a fixed set-point. The suitability of the controller formulation that we propose is supported by the fact that it follows well-known adaptive control techniques ( Slotine and Li, 1991 ) and previous works in other application contexts ( Aghajanzadeh et al, 2017 , 2018 ). Hence, in this study, the relationship between the grasped point and the controlled point is assumed as follows: …”

“…The proposed method does not need prior knowledge of any model parameters of the object and works in real-time. There are several contributions in our work: • We take as starting point an adaptive control scheme proposed for biomedical applications ( Aghajanzadeh et al, 2017 , 2018 ) and we extend it to a novel problem which is deformable object manipulation. • To the best of our knowledge, the existing adaptive approaches ( Navarro-Alarcon et al, 2014 ; Navarro-Alarcon and Liu, 2017 ; Zhu et al, 2018 ; Lagneau et al, 2020 ) simply reach a fixed set-point without considering any deformation trajectory while our controller can be used to track a desired dynamic evolution of the state.…”

“…• We take as starting point an adaptive control scheme proposed for biomedical applications ( Aghajanzadeh et al, 2017 , 2018 ) and we extend it to a novel problem which is deformable object manipulation.…”

Efficient prediction of drug–drug interaction using deep learning models

“…An efficient combination has a huge impact on cancer patients through the fusion of various drug–drug combinations [6]. The DDI combinations help cancer patients against cancer pathogens attacking the body [7] like viruses [8] and bacteria [9] in different ways [10]. The specific drug targets specific intrusive pathogens and provides resistance to the host from the specific antigen [11].…”

Robust control of HIV infection by antiretroviral therapy: a super‐twisting sliding mode control approach

“…A non‐linear robust adaptive SMC strategy is presented for the influenza epidemics in the presence of model uncertainties in [ 30 ]. A non‐linear robust adaptive Lyapunov‐based control strategy was designed in [ 31 ] for the antiviral drug therapy of the hepatitis B virus infection with different cases of uncertainties. SMC based on the super‐twisting algorithm (STA) stabilises the blood glucose concentration of a diabetic patient at the desired level [ 32 , 33 ].…”