# An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices
## 4 Discussion  
One of the primary goals needed to improve LVAD technology involves developing a control strategy that automatically adjusts pump speed to meet changes in metabolic demand. In the normal functioning of the heart, the pumping process is subject to the Frank-Starling mechanism [8; 9]. This mechanism ensures that the left ventricular stroke volume is adjusted to compensate for left ventricular end-diastolic pressure-volume changes. In order to achieve this theory, in this work, we have chosen the pump flow as a significant parameter for the design of the control system. The elastance function was also chosen as an input signal to acquire a rate of change in the flow that synchronizes the HF patients. The simulation results demonstrated that this function could simulate the proposed flow to balance the physiological status of HF patients, as shown in Figure 5. However, other studies chose the pump differential pressure as an essential control parameter to obtain significant results to simulate HF status [10; 12]. In general, LVADs show limited preload sensitivity, indicating their inability to detect or perceive the amount of blood they receive [9]. Thus, it is critical to implement a pump control approach to maintain a safe operating range, where pump flow corresponds to right heart output [36]. Inadequate action may lead to ventricular collapse caused by excessive pumping or decreased preload, reversed pump flow (regurgitation), and pulmonary edema due to insufficient pumping and a consequent decrease in applied differential pressure [37; 38]. Therefore, it is essential to approximate direct values of pump flow to obtain the control approach. To achieve this aim, we selected the elastance function as an essential input signal and performed designs so as to obtain a rate of change in the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Healthy**} & \multicolumn{2}{c}{**HF + LVAD**} \\ \cline{3-4}  & & **Exercise** & **Rest** \\ \hline Aortic pressure (mmHg) & 120 & 77 & 105 \\ \hline Left ventricle pressure (mmHg) & 120 & 81 & 97 \\ \hline Output flow (\(Q_{p}\)) (L/min) & 5.5 & 2 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CVS variables from the simulation results. Figure 8: Hemodynamic variables. **(a)**: pressure vs. time; **(b)**: flow vs. time. flow that synchronizes the work of HF patients. The features of this function ensure a minimum average pump flow rate or achieve a maximum differential pressure restriction that requires the careful evaluation of CVS parameters [39]. Eliminating disturbances and parameter variation during controller operation is the significant challenge often found in the operation of this type of pump. Therefore, many control systems are designed to minimize or eliminate these issues [11]. In this regard, this work utilized the neural network method to eliminate any presence of disturbance or parameter variation. This design method introduced the numerical analysis based on PP-SMC while being subject to various physiological situations. According to the findings, the performance of PP-SMC is superior to other control methods [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. For instance, the findings presented in Figures 5-7 and Table 2 demonstrate that the neural PP-SMC can increase tracking performance because it can adaptively alter its parameters based on CVS parameters. Our results also indicate that employing this control strategy will eventually decrease error tracking between the reference and estimated outputs. Due to the reduced elastance on control tracking performance, the estimated pump flow output will follow the reference output flow of 1.5 to 7 L/min. This may cause fewer amplitude oscillations and result in fewer ventricular auctions and pulmonary congestion occurrences.