Swarm optimization approach to design PID controller for artificially ventilated human respiratory system

Background and Objective: An artificially ventilated human respiratory system is used to help breathing of a patient with respiratory problem. The level of oxygen is maintained stable by controlling the airway pressure in the lungs mechanism with the help of medical ventilator. For pressure control in a ventilator, the airway pressure in lungs mechanism is controlled by a motor driven piston mechanism. The optimal setting of controller parameters of a respiratory ventilator system depends on many factors of a patient such as physical condition of patient, need of oxygen, age of a patient etc. Therefore, computer operated algorithm based artificial ventilation system becomes most popular for its better performance, efficiency, and easy control mechanism. In this paper, a simple swarm optimization based controller design approach is systematically verified to design suitable controller for pressure controlled artificially ventilated human respiratory system. A modified constricted class topper optimization (C-CTO) algorithm is proposed for tuning the controller in an artificial ventilator system. Methods: A pressure controlled ventilation (PCV) model has been considered. A proportional-integral derivative (PID) controller structure is considered for the PCV. Three different optimization approach (Particle swarm optimization (PSO), class topper optimization (CTO) and a modified constricted class topper optimization (C-CTO)) are verified one by one for the purpose of tuning PID controller for PVC system. Results: The performances of swarm based controller in PCV system for three different cases are examined in terms of settling times and maximum overshoot of the system. Conclusions: The swarm based optimization approach is improving the dynamic response of pressure control artificially ventilated human respiratory system. In this paper, a simple piston-motor driven lung mechanism is applied to verify the swarm based approach, but this approach can further be checked in the future for more complex human lungs artificially ventilated system. (c) 2020 Elsevier B.V. All rights reserved.