Design of depth of anesthesia controllers in the presence of model uncertainty

A major obstacle in the design of controllers to regulate the depth of anesthesia (DoA) consists in the high model uncertainty due to inter-patient variability. Surprisingly, the use of control design methods that explicitly tackle this problem is almost absent from the literature on automatic control of anesthesia. In this work, a DoA controller is designed taking into account model uncertainty to comply with robust stability and robust performance specifications for a patient population undergoing elective general surgery, with hypnosis induced by the drug propofol. Due to its Wiener nonlinear structure, the DoA model can be linearized around a given operating point. Therefore, using a database with 18 patient models, a non-parametric description of uncertainty for a linearized model is first performed. By using H_∞ design methods, a continuous linear controller is then designed so as to ensure robust stability and performance within the uncertainty bounds defined. The controller that results from this procedure is approximated by a controller with a lower order that, in turn, is redesigned in discrete time for computer control application. The final result is tested in nonlinear realistic patient models, with acceptable closed-loop results.