  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 situations. 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 adaptive 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 problem of estimation is updated by a series of finite-time
optimization problem which can be solved by backwards sweep Riccati method in
real time without employing any iteration techniques. The simulation results
demonstrates the fact that proposed algorithm is able to estimate unknown
constant/time-varying parameters of HIV/AIDS model effectively and provide a
unique, adaptive solution methodology to an important open problem.
