Development of Neuro-Fuzzy Controller for Pressure Control of Artificial Respiratory System

Artificial ventilators or Mechanical ventilators (MVs) are critical medical devices used to support or replace spontaneous breathing in patients, particularly during surgical procedures or in ICU settings for those with respiratory distress. This study examines the functionality of a piston-driven ventilator, often utilized as an anesthesia ventilator operating in volume-controlled modes. Unlike traditional volume control, a pressure-controlled mode can be advantageous in specific scenarios, such as in laparoscopic surgery, by providing better compliance during pneumoperitoneum, enhancing oxygen delivery, and reducing stress post-surgery. Accurate pressure tracking is crucial in pressure-controlled ventilation (PCV) to minimize risks such as ventilator-induced diaphragmatic dysfunction. To address this, a neuro-fuzzy controller is proposed for precise pressure tracking in artificial ventilators. By integrating fuzzy logic and neural network elements, the neuro-fuzzy controller aims to maintain a responsive and adaptable pressure profile, tailored to the patient's needs. This study evaluates the controller's performance under various simulated scenarios, benchmarking its effectiveness against current methods in ventilator control