Teaching-Learning-Based Optimization for Robust and Adaptive Control of Mechanical Ventilation Systems

Mechanical ventilation is vital for patients with respiratory disorders, requiring accurate airflow control to adapt to changing lung conditions. PID controllers are widely used, but traditional tuning methods are time-consuming and often inadequate under disturbances. This study compares five metaheuristic optimization techniques Teaching-Learning-Based Optimization (TLBO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), for automatic PID tuning for the mechanical ventilation system under normal and obstructive conditions. Evaluation using MSE, MAE, and R 2 shows that TLBO achieved the best performance, while GA, PSO, ACO and GWO offered higher frequency oscillations. The TLBO-optimized PID demonstrated robustness against sudden disturbances, highlighting the potential of metaheuristic tuning to improve ventilator adaptability and efficiency.