Multivariable Modeling and Control of the Response to Anesthesia

Abstract

To address these shortcomings, this dissertation proposes the use of piecewise-linear multivariable models of the effects of anesthetic and stimuli inputs on patient vital signs. These models are developed by utilizing data-based system identification methods. Different model structures such as linear time invariant (LTI), piecewise linear switching and parameter-varying, are investigated and verified. Robust gain scheduled controllers are constructed and simulated using a linear parameter varying (LPV) framework. The use of multivariable state-space models for patient modeling is novel in this research area, as is the proposed use of piecewise-linear models. The modeling results indicate that the proposed piecewise-linear models yield improved simulation responses over traditional PK-PD models, in comparisons made with respect to single-output effects. Further, individual models may provide reasonable central models in the sense that simulated output responses obtained by applying the input data set for one volunteer to the piecewise-linear models for other volunteers produces an acceptable fit to the output data for the first volunteer. Similar results are found for our constructed individual controllers when applied as a generic controller for other volunteer data.