Controlled delivery of intravenous (IV) anesthetics aims at fast and safe achievement and maintenance of a suitable depth of hypnosis (DOH), by ensuring appropriate effect site (i.e. brain) exposure to the drug. Today, such drugs are regularly injected by Target Controlled Infusion (TCI) systems, piloted by an open-loop algorithm based on Pharmacokinetic (PK) models. Yet the inaccuracy of concentration prediction of current TCI can reach up to 100%. The situation could be improved by closing the loop with sensors providing regular real measurements of the anesthetic concentration in body fluids. In this paper we present a closed-loop algorithm based on the classic open-loop algorithm combined with a Kalman filter. The latter estimates plasma drug concentration based on PK model and sensor measurements. The estimates are then used in the open-loop algorithm. To validate our approach measurements are generated by means of modulating the population-based plasma concentration values with the maximum inter- and intrapatient variability of the statistical Eleveld׳s (Eleveld et al., 2014) PK model. This allows us to stress the system to a maximum level prior to testing it on patients. We also perform robustness analysis of this algorithm by accounting for realistic measurement periods and delays.