Control of Human Immunodeficiency Virus (HIV) Dynamics With Parameter Uncertainties

This work considers a 3-state nonlinear model with two inputs for the controlled dynamics of the human immunodeficiency virus (HIV). The three states represent the number of healthy and unhealthy T-cells as well as the number of free virus particles in a person’s body. The two inputs represent two different types of anti-retroviral drugs that are available to treat a person infected with the virus. These inputs can be used to create a stable nominal point that is much healthier for the patient than the open-loop stable equilibrium point. The goal of this paper is to use the inputs, which are subject to constraints, to efficiently and accurately reach the desired nominal point despite parameter uncertainties. We have designed an integral sliding mode control law to achieve this goal, and simulations are presented to demonstrate the performance of the controller.