In this thesis, a model-based closed-loop fluid resuscitation controller using mean arterial pressure (MAP) feedback is designed and later evaluated on an in-silico testbed. The controller is based on a subject specific model of blood volume and MAP response to fluid infusion. This simple hemodynamic model is described using five parameters only. The model was able to reproduce blood volume and blood pressure response to fluid infusion using an experimental dataset collected from 23 sheep and is therefore suitable to use for control design purposes. A model-reference adaptive control scheme was chosen to account for inter-subject variability captured in the parametric uncertainties of the underlying physiological model. Three versions of the control algorithm were studied under different measurement availability scenarios. In-silico evaluation of the three controllers was done using a comprehensive cardiovascular physiology model on a cohort of 100 virtually generated patients. Results clearly show that a tradeoff exists between tracking and estimation performance depending on measurement availability.