Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling

Abstract

This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input-output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box-Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure.