Article
Version 1
Preserved in Portico This version is not peer-reviewed
An Intelligent Physiological Control Method Using Sliding Mode Neural Network for Left Ventricular Assist Device
Version 1
: Received: 5 July 2023 / Approved: 5 July 2023 / Online: 6 July 2023 (10:15:31 CEST)
A peer-reviewed article of this Preprint also exists.
Bakouri, M. An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices. Appl. Syst. Innov. 2023, 6, 97. Bakouri, M. An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices. Appl. Syst. Innov. 2023, 6, 97.
Abstract
The technology of left ventricular assist devices (LVADs) requires developing and implementing an intelligent control systems to optimize pump speed to achieve a physiological metabolic demands for heart failure (HF) patients. This work aimed to present an advanced control algorithm to drive an LVAD under different physiological conditions. Pole placement method in conjunction with of sliding mode control approach (PP-SMC) was utilized to design and construct the proposed control method. In this design, the method was adopted to use neural networks to eliminate system uncertainties of disturbances. An elastance function was also developed and used as an input signal to mimic the physiological perfusion of HF patients. Two scenarios ranging from rest to exercise were introduced to evaluate the proposed technique. In this technique, a lumped parameter model of cardiovascular system (CVS) was used for this evaluation. The results demonstrated that the designed controller was robustly tracking the input signal in the presence of the system parameter variations of CVS. In both scenarios, the proposed method shows that the controller automatically drive the LVAD with a minimum flow of 1.7 L/min to prevent suction and 5.7 L/min to prevent over-perfusion.
Keywords
pole placement; sliding mode control; left ventricular assist devices; cardiovascular system; heart failure
Subject
Engineering, Bioengineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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