Hybrid Optimization based Controllers for Auto Respiration System for Patients

Artificial ventilation is frequently implemented to address a variety of respiratory conditions in humans. Smooth respiration necessitates the maintenance of the oxygen level in corona patients, a task that is exceedingly challenging. In order to achieve this state, the respiration system, which is powered by a motor and features a piston mechanism, must regulate air pressure. Hybrid optimization techniques are employed to optimize controller parameters and develop an automatic respiration system model. Hybrid Fminsearch Simulated Annealing-based PID controller and hybrid Ant Colony Optimization-Genetic Algorithm-based PID controller with ISE, IAE, and ITAE as objective functions were implemented to create a stable controller. The Ant Colony Optimization - Genetic Algorithm based PID controller with ITAE as the objective function achieves a superior outcome when the time domain and error indices are compared.