Robust adaptive control of hypnosis during anesthesia

A closed-loop controller for hypnosis was designed and validated on humans at our laboratory. The controller aims at regulat-ing the Bispectral Index (BIS)- a surro-gate measure of hypnosis derived from the electroencephalogram of the patient- with the volatile anesthetic isoflurane administered with a closed-circuit breathing system. The control algorithm consists of a cascaded Inter-nal Model Controller (IMC) where the master loop aims at regulating BIS. The slave loop tracks endtidal concentration references pro-vided by the master controller. In this paper, a new tuning method is presented. First, a ro-bust design procedure which guarantees sta-bility of the slave controller despite paramet-ric uncertainties is described. Then, we will demonstrate how the estimation of the drug’s equilibration constant ke0 greatly improves performance if the estimated value is used to update the models in the control scheme. In order to do so, an identification scheme for ke0 is proposed, which requires estimation of the drug’s time to peak effect tpeak. The identifica-tion algorithm requires few modeling assump-tions and guarantees convergence. Simulation results are presented, which quantify both the performance of the identification scheme and the improvement of the closed-loop control performance