•Multi-parametric model predictive control (mp-MPC) for hypnosis during anesthesia.•Combined control design of mp-MPC and online parameter estimation.•Evaluation of control strategy for uncertainty originated by patient variability.•Controller's dynamics are adjusted to the individual patient's sensitivity.•Closed loop control validation for induction and disturbance rejection. This paper addresses inter- and intra-patient variability in the context of automated drug delivery during anesthesia. A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability. The parameter with the highest sensitivity, C50 the effect site concentration at 50% drug effect, is estimated online. The performance of the closed loop control design is shown for induction and maintenance of volatile anesthesia. In the maintenance phase, the control strategy is evaluated for predefined disturbances that are commonly occurring during surgery. The presented approach shows an improved performance compared to the nominal MPC controller under uncertainty.