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An increasing number of control techniques are introduced to HIV infectionproblem to explore the options of helping clinical testing, optimizing drugtreatments and to study the drug resistance situations. In such cases,complete/accurate knowledge of the HIV model and/or parameters is critical notonly to monitor the dynamics of the system, but also to adjust the therapyaccordingly. In those studies, existence of any type of unknown parametersimposes severe set-backs and becomes problematic for the treatment of thepatients. In this work, we develop a real-time adaptive nonlinear recedinghorizon control approach to aid such scenarios and to estimate unknownconstant/time-varying parameters of nonlinear HIV system models. For thispurpose, the problem of estimation is updated by a series of finite-timeoptimization problem which can be solved by backwards sweep Riccati method inreal time without employing any iteration techniques. The simulation resultsdemonstrates the fact that proposed algorithm is able to estimate unknownconstant/time-varying parameters of HIV/AIDS model effectively and provide aunique, adaptive solution methodology to an important open problem.

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