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The goal of target-controlled delivery of intravenous (IV) anesthetics is the achievement and maintenance of a suitable depth of hypnosis (DOH) in a fast and safe manner, where DOH is associated with a certain effect site (i.e. brain) drug concentration. Nowadays, the delivery of anesthetic drugs is performed by target-controlled infusion (TCI) pumps adjusting the delivery rate using an algorithm based on pharmacokinetic (PK) models having no feedback. However, the inaccuracy of concentration prediction using this PK model for certain individuals can be up to 100%. In this chapter, we show that the precision of anesthesia delivery can definitely be improved by realising a feedback loop with sensors able to provide measurements of the anesthetic concentration in body fluids in real time. We present two possible approaches for building the control feedback loop using plasma concentration measurements: one representing the classic method in pharmacokinetics based on Bayesian inference and another one being an example of classic method in control theory based on Kalman filter. The first one performs real-time re-estimation of PK model parameters with each new measurement, while the latter one estimates the offset values for drug concentration correction. The adjusted concentration values are further used to compute the personalised delivery rate using the classic TCI algorithm. To validate the algorithms’ robustness, we simulate measurements covering the maximum space of possible values using inter- and intra-patient variability of the statistical Eleveld’s (Eleveld, Proost, Cortinez, Absalom, Struys, Anesth Analg 118(6):1221–1237, 2014, [8]) PK model. This allows one to disturb the system to its extreme before testing it on patients. We provide the robustness analysis of these algorithms with respect to realistic measurement periods and delays.