Robust Drug Treatment for HIV-1 Infection Model with Completely Unknown Parameters

Abstract

A robust drug treatment algorithm is proposed to deal with uncertain model parameters in HIV infection models. Taking into account the fact that the parameter values of an HIV-infection model depend on the patient’s infection condition, the treatment goal is set to suppress the virus concentration to a target value even if significant uncertainties exist in model parameters. Since the measurement of all state variables is not feasible in real clinical situations, the proposed treatment algorithm is implemented by the measurement of the virus concentration only. Compared with the previous research works, a key idea of the proposed scheme is to control the efficacies of protease inhibitors (PIs) instead of reverse transcriptase inhibitors (RTIs). By varying the efficacy of PIs, it is established that the treatment goal can be achieved with the help of nonlinear disturbance observer technique. To show the effectiveness of the proposed method, simulation studies are provided. It is shown that the proposed controller achieves robust performance for reducing the virus concentration even when all parameters have uncertain values in the range between 20% and 500% of their nominal values.