Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support

Abstract

In the early stage of the 21st century, humankind is facing high medical risks. To the best of our knowledge, there is currently no efficient way to stop chains of infections, and hence citizens suffer significantly increasing numbers of diseases. The most important factor in this scenario is the lack of necessary equipment to cure disease and maintain our living. Once breath cannot be guaranteed, humans find themselves in a dangerous state. This study aimed to design, control, model, and simulate mechanical ventilator that is open-source structure, lightweight, and portable, which is proper for patients to cure themselves at home. In the scope of this research, the hardware platform for the mechanical design, implementation of control rules, and some trials of both simulations and experiments are presented as our methodology. The proposed design of ventilator newly features the bioinspired mechanism, finger-like actuator, and flow rate-based control. Firstly, the approximate evaluation of the lung model is presented with some physiological characteristics. Owing to this investigation, the control scheme was established to adapt to the biological body. Moreover, it is essential for the model to be integrated to determine the appropriate performance of the closed-loop system. Derived from these theoretical computations, the innovative concept of mechanical design was demonstrated using the open-source approach, and the real-world model was constructed. In order to estimate the driving torque, the hardware modeling was conducted using mathematical expressions. To validate the proposed approach, the overall system was evaluated using Matlab/Simulink, and experiments with the proposed platform were conducted in two situations: 20 lpm as a reference flow rate for 4 seconds and 45 lpm for 2.5 seconds, corresponding to normal breath and urgent breath. From the results of this study, it can be clearly observed that the system’s performance ensures that accurate airflow is provided, although the desired airflow fluctuates. Based on the test scenario in hardware, the RMS (root-mean-square) values of tracking errors in airflow for both cases were 1.542 and 1.767. The proposed design could deal with changes in airflow, and this machine could play a role as a proper, feasible, and robust solution to support human living.