Particle Swarm Optimization based PID-Controller Design for Volume Control of Artificial Ventilation System

Artificial ventilation has become a necessary means for medical purposes. With an increase in the reliability of such devices, the need for designing a proper controller is growing. The main purpose of this paper is to design a particle swarm optimization (PSO) based PID-controller to control the volume of the artificial ventilation systems. PSO algorithm is used to tune the gains of the PID controller. It aims at improving the dynamic stability of an artificial ventilation system. A step signal is considered as the desired signal for the system which acts as a reference signal for the controller. With the optimum parameters of the designed controller, the dynamic stability of the volume control ventilation system is improved.

[1]  Sita Radhakrishnan,et al.  Analysis of parameters affecting blood oxygen saturation and modeling of fuzzy logic system for inspired oxygen prediction , 2019, Comput. Methods Programs Biomed..

[2]  A randomized controlled trial comparing the ventilation duration between adaptive support ventilation and pressure assist/control ventilation in medical patients in the ICU. , 2015, Chest.

[3]  Weng Khuen Ho,et al.  Integral-Square-Error Performance of Multiplexed Model Predictive Control , 2011, IEEE Transactions on Industrial Informatics.

[4]  S. Kopp,et al.  Intraoperative use of low volume ventilation to decrease postoperative mortality, mechanical ventilation, lengths of stay and lung injury in adults without acute lung injury. , 2018, The Cochrane database of systematic reviews.

[5]  M. A. Borrello Adaptive inverse model control of pressure based ventilation , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[6]  Thomas Stützle,et al.  Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm , 2009, IEEE Transactions on Evolutionary Computation.

[7]  M. Janaki,et al.  A Survey of Control Algorithms Used In Physiological Closed Loop Control for Oxygen Therapy , 2019 .

[8]  S. Gomes,et al.  Volume‐controlled Ventilation Does Not Prevent Injurious Inflation during Spontaneous Effort , 2017, American journal of respiratory and critical care medicine.

[9]  Maolin Cai,et al.  Numerical simulation of volume‐controlled mechanical ventilated respiratory system with 2 different lungs , 2017, International journal for numerical methods in biomedical engineering.

[10]  O. Merroun,et al.  Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators , 2019, Renewable Energy.

[11]  E. Ochroch,et al.  Intraoperative use of low volume ventilation to decrease postoperative mortality, mechanical ventilation, lengths of stay and lung injury in patients without acute lung injury. , 2015, The Cochrane database of systematic reviews.

[12]  T Bouillon,et al.  Model-based control of mechanical ventilation: design and clinical validation. , 2004, British journal of anaesthesia.

[13]  Zwe-Lee Gaing A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[14]  W. Heinrichs,et al.  An adaptive lung ventilation controller , 1994, IEEE Transactions on Biomedical Engineering.

[15]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[16]  S. Leonhardt,et al.  Control applications in artificial ventilation , 2007, 2007 Mediterranean Conference on Control & Automation.

[17]  Leonardo A. B. Tôrres,et al.  Iterative Learning Control Applied to a Recently Proposed Mechanical Ventilator Topology , 2019 .

[18]  Robert Babuska,et al.  Computer-controlled mechanical simulation of the artificially ventilated human respiratory system , 2003, IEEE Transactions on Biomedical Engineering.