Long-term analysis of HIV infection therapy with cubature Kalman filtering-based predictive control

Mathematical model-based analysis and control of human immunodeficiency virus (HIV) infection have recently provided important advantages in medicine. In this paper, firstly the literature on mathematical models and applied control methods will be surveyed to evaluate the HIV models and therapy. Secondly, a cubature Kalman filter-based nonlinear model predictive control is proposed for the multi-input multi-output control of HIV infection for decreasing the cost of sensory devices and increasing the efficiency of therapy. By doing so both unmeasurable states and personalized parameters of the HIV infection are jointly estimated in a control process to generate suitable drug dosages. In the literature, the applied drug dosages are in continuous or on/off levels. For a practical application of continuous drug dosage-level, it has been discretized into 10 levels of full dosage level. Therefore, the applied drug dosages are in piecewise-continuous levels instead of continuous values or on/off levels. The proposed observer–controller configuration has been applied to the strong and moderate therapy levels of long-term non-progressive as well as fast-progressive patients with personalized parameters, where the application results are discussed for 1-, 5-, 10- and 20-year periods. The computational results show that satisfactory performances are obtained for future applications in terms of the root-mean-squared error of the estimation and control, and in terms of the integral sum of the control input.