Date of Award
Summer 1990
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Brown, Ronald H.
Second Advisor
Jeutter, Dean C.
Third Advisor
Feng, Xin
Abstract
This thesis is concerned with the selection and measurement of actuating signals which can serve as inputs to an adaptive control system responsible for regulating an electric total artificial heart (TAH). The techniques developed represent a new application of systems identification in the field of TAH control. The focus of this research project is on the problem of identifying and tracking systemic arterial parameters, in real-time, on the basis of signals measured noninvasively. Identification models, representing the systemic arterial system, are developed from existing work in the area of cardiovascular modeling. The components of these models are physically significant, representing the overall hydraulic properties of the systemic arterial system. A new method of parameterizing the identification models is developed which operates on the basis of aortic pressure and flow measurements acquired exclusively during systole. The estimator is a modified recursive least squares (RLS) algorithm which utilizes a variable forgetting factor to track time-varying parameters. Covariance resetting is used to compensate for abrupt parametric changes by monitoring the prediction error. The issue of reduced order modeling is treated as a bounded error problem and a dead-zone is employed to increase the robustness of the estimator in the presence of unmodeled dynamics. The result of the interruption of pressure and flow measurements during diastole is explored and modifications to the standard RLS algorithm are designed which guarantee the convergence and consistency of parameter estimates with an appropriate input signal. Results from model-to-model experiments verify the consistency of the estimates and the ability of the estimator to track both slowly and acutely varying parameters. The effect of estimating the parameters of a higher-order simulation model is explored and the estimation algorithm is shown to track time-varying parameters in the presence of unmodeled dynamics.
Recommended Citation
Ruchti, Timothy L., "Identification of Systemic Arterial Parameters for Control of An Electrically Actuated Total Artificial Heart" (1990). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4038.
https://epublications.marquette.edu/theses/4038