Design and Comparison of Hybrid Optimization controllers for Artificial Respiration System

Artificial ventilation is vital for managing respiratory disorders, especially sustaining oxygen levels in severe patients. A motor-driven respiratory system with a piston mechanism must effectively manage volume to ensure smooth respiration. This study optimizes controller parameters using hybrid methodologies to create an automated breathing system. Two PID controllers—Hybrid Fminimax-Simulated Annealing and Hybrid Fmincon-Genetic Algorithm—were used with ISE, IAE, and ITAE as objectives. A comparative investigation reveals that the Hybrid Fmincon-Genetic Algorithm-based PID controller optimized with ITAE has the highest stability and performance.