Constrained model predictive control of hypnosis

This thesis investigates the design and performance of a model predictive controller (MPC) for the automatic control of hypnosis. It constitutes the first step towards automatic control of anesthesia with constraints on important parameters such as drug concentrations in the body and hemodynamic variables such as blood pressure. The literature suggests, that closed-loop control of anesthesia can significantly reduce drug consumption and lessen recovery times, thus improving the safety and quality of anesthesia care while reducing costs. However, automation of anesthesia is challenging because of shortcomings associated with drug-response modeling, in particular limited data for children and disagreement between published models, inadequate predictive capacity of models owing to inclusion of monitor dynamics in the models, and significant inter/intra patient variability and uncertainty in models. The first part of this thesis introduces a new approach to dose-response modeling and presents models with different clinical end-points for propofol in children and adults. This thesis also presents a new monitor-decoupledmodel of propofol pharmacodynamics (PD) where the monitor model is clearly excluded from the identified PD. The second part of the thesis concentrates on design of a constrained MPC for hypnosis. While the anesthesia closed-loop concept has already been investigated in the past, there is still a need for a closed-loop control system that explicitly includes robustness in the design step, allows constraints on drug concentrations and physiological parameters, and can incorporate multivariable control of multi drug and multi sensor systems. In this thesis, robust MPC controllers are presented for closed-loop control of depth of hypnosis in adults and children. Robustness in the presence of inter-patient variability is taken into account in the controller design. A novel idea is introduced on how to define and implement physiological constraints in closed-loop control of hypnosis using MPC with a parallel PKPD model. Evaluation of the proposed MPC meets the design specifications and shows that the required robustness against patient uncertainty is achieved and the proposed safety constrained control strategy can potentially reduce the risk of under/over-dosing for most patients by providing controller enforced safety bounds without sacrificing the performance of the closed-loop control system.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat