2004
DOI: 10.21236/ada441233
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Abstract: We consider optimal dynamic multidrug therapies for human immunodeficiency virus (HIV) type 1 infection. In this context we describe an optimal tracking problem attempting to drive the states of the system to a stationary state in which the viral load is low and the immune response is strong. We consider optimal feedback control with full state as well as with partial state measurements. In the case of partial state measurement, a state estimator is constructed based on viral load and T-cell count measurements… Show more

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Cited by 4 publications
(5 citation statements)
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References 33 publications
(88 reference statements)
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“…In Bonhoeffer et al (2000), the authors present simulations, which suggest that a model with this immune reponse structure and a latently infected cell compartment can exhibit transfer between "healthy" and "unhealthy" locally stable steady states via STI, making it a good candidate for our investigation. (Indeed, further investigations (Adams et al, 2004;Banks et al, 2006) with (1) substantiate that active control through optimal or on off 0 1 time(t) u(t) Fig. 4 Sample control input (treatment protocol) u(t) representing structured treatment interruption.…”
Section: Model Descriptionmentioning
confidence: 93%
“…In Bonhoeffer et al (2000), the authors present simulations, which suggest that a model with this immune reponse structure and a latently infected cell compartment can exhibit transfer between "healthy" and "unhealthy" locally stable steady states via STI, making it a good candidate for our investigation. (Indeed, further investigations (Adams et al, 2004;Banks et al, 2006) with (1) substantiate that active control through optimal or on off 0 1 time(t) u(t) Fig. 4 Sample control input (treatment protocol) u(t) representing structured treatment interruption.…”
Section: Model Descriptionmentioning
confidence: 93%
“…In this regard the stability analysis is investigated in [16], [17], [25], [26], [28] and [30], and a variety of control strategies is applied to HIV-1. For instance, feedback control in [7]- [9] and [20], nonlinear theory based control in [14]- [15] and [29], model predictive control in [18], [27], optimal control in [10] [22] [31] [32] and [36] and intelligent control in [59] [63] can be noticed.…”
Section: On the Other Hand Recent Years Have Witnessed Growing Interementioning
confidence: 99%
“…This aspect of the model is based on a Michaelis–Menten nonlinearity saturation as proposed by Bonhoeffer et al 21. This specific model has been studied in 3, 8, 22.…”
Section: Hiv Modelmentioning
confidence: 99%
“…Techniques based around solutions of Ricatti equations may be augmented to solve nonlinear control problems; however, these techniques require that the model be of a certain form and can require frequent state knowledge or estimation. For an example of this sort of technique applied to HIV control, see 8. A potential solution involves using Bellman's principle of optimality; however, this involves solving a nonlinear partial differential equation which, in general, is computationally challenging.…”
Section: Introductionmentioning
confidence: 99%
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