An adaptive fractional order optimizer based optimal tilted controller design for artificial ventilator

Abstract

Abstract Artificial ventilators are vital respiratory support systems in the field of medical care, especially for patients in critical condition. It is crucial to make sure the ventilator keeps the intended airway pressure because variations might be harmful to the brain and lungs. Thus, achieving accurate pressure tracking is a primary objective in designing optimal controllers for pressure‐controlled ventilators (PCVs). To address this need, a novel approach is proposed: a mixed integer tilted fractional order integral and integer order derivation controller tailored for PCV systems. The gains of different parameters of the proposed controller are optimized using an adaptive chaotic search fractional order class topper optimization algorithm, augmented with a Gaussian‐based mutation operator. Moreover, the controller is designed to minimize oscillations in its output signal, thereby mitigating physical risks and reducing the size of actuators required. The efficacy of the optimized controller is further examined across various scenarios, including different lung resistances and compliances across different age groups of patients. Additionally, the impact of endotracheal tube resistance on air pressure is assessed as a potential disturbance in the PCV system. Through comprehensive testing, the proposed controller demonstrates superior performance in accurately tracking airway pressure to the desired levels. Across all evaluated cases, the proposed controller structure and accompanying algorithm outperform existing solutions. Notably, improvements are observed in system response time, overshoot, and settling time. This underscores the significance of employing advanced control strategies to enhancing the functionality and safety of PCV systems in medical settings.