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

TL;DR

This paper presents a real-time adaptive control method using nonlinear receding horizon control to estimate unknown parameters in HIV/AIDS models, aiding treatment optimization and resistance management.

Contribution

It introduces a novel real-time estimation approach for unknown HIV model parameters using a non-iterative receding horizon control technique.

Findings

Effective estimation of constant and time-varying parameters demonstrated in simulations.

Provides a real-time, iterative-free solution for HIV parameter estimation.

Enhances adaptive treatment strategies for HIV/AIDS patients.

Abstract

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.