Abstract
This research employs parametric polynomial techniques to determine the parameters of a dynamic fluid control system. The process of system identification involves constructing a mathematical model of a dynamic system using measured data in the time or frequency domain. The approach involves fitting a polynomial function to the input-output data, with the polynomial parameters representing the unknown parameters of the system. The objective is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid, and coronary artery signals were utilized in modeling and testing studies of the flow system during parametric model studies, as the system forms the basis for hemodynamic research. The Autoregressive model with external input (ARX), Autoregressive moving average model with external input (ARMAX), Box-Jenkins (BJ), Output-Error (OE), and State Space Model (SSM) parametric models were utilized in the modeling process, and the transfer function of the most successful parametric model was calculated in the mathematical performance analysis. Flow control devices such as the AC motor and centrifugal pump were employed. The transfer function that exhibited the most successful performance was used in observer design as the Luenberger Controller. An innovative closed-loop control system was achieved using the Luenberger structure.