In this work we present different design strategies towards model based simultaneous multiparametric model predictive control and state estimation for intravenous anaesthesia. We first present a detailed compartmental mathematical model featuring a pharmacokinetic and a pharmacodynamics part. Due to unavailability of data and information, different estimation techniques are formulated and implemented. Furthermore these estimation techniques are implemented simultaneously with multiparametric model predictive controllers and tested for real patient data under the assumption that the output is either noise free or corrupted by noise. The derived control schemes are able to deal with two of the main challenges in controlling the depth of anaesthesia: (i) model nonlinearity and (ii) inter-and intra-patient variability. (C) 2017 Elsevier Ltd. All rights reserved.