Estimation of CD4+ T Cell Count Parameters in HIV/AIDS Patients Based on Real-time Nonlinear Receding Horizon Control

An increasing number of control techniques are introduced to HIV infection problem to explore the options of helping clinical testing, optimizing drug treatments and to study the drug resistance. In such cases, complete/accurate knowledge of the HIV model and/or parameters is critical not only to monitor the dynamics of the system, but also to adjust the therapy accordingly. In those studies, existence of any type of unknown parameters imposes severe set-backs and becomes problematic for the treatment of the patients. In this work, we develop a real-time nonlinear receding horizon control approach to aid such scenarios and to estimate unknown constant/time-varying parameters of nonlinear HIV system models. For this purpose, the estimation procedure is reduced to a series of finite-time optimization problem which can be solved by backwards sweep Riccati method in real time without employing any iterative techniques. The simulation results demonstrate that proposed algorithm is able to estimate, effectively, unknown constant/time-varying parameters of HIV/AIDS model with disturbance and provide a unique, adaptive solution to an important open problem.