Human Immunodeficiency Virus (HIV) disease has been one of the most significant global health challenges in recent decades. Despite therapeutic advances, controlling its dynamics remains a fundamental issue. The biological complexities of the disease, along with limited access to real-world data, underscore the necessity of mathematical modeling an‎d the design of precise nonlinear control strategies. In this study, a five-variable dynamic model of HIV disease, including healthy CD4⁺ T cells, infected cells, free virus, CD8⁺ T cells, an‎d activated cells, was employed. Then, to enhance treatment efficiency an‎d achieve effective viral regulation, a state-dependent Riccati equation (SDRE)-based optimal controller was designed to intelligently adjust the dosage of antiretroviral drugs according to the immune system’s condition. Considering the practical limitations in measuring internal physiological states, the proposed approach was complemented by state estimators based on the Extended Kalman Filter (EKF) an‎d the Unscented Kalman Filter (UKF). The performance of the proposed method was eva‎luated by comparing its results with those obtained from a feedback linearization controller. The analysis demonstrates that the optimal control strategy not only effectively reduces the viral load but also maintains immune system stability in the presence of disturbances an‎d uncertainties.

محسن مجيري فروشاني , فريد شيخ الاسلام

احسان روحاني

مريم ذكري , مرضيه كمالي