Imagen de Portada

Adaptive Bayesian optimization for proportional derivative control in double-acting piston pump ventilators

Guardado en:
Publicado en:SN Applied Sciences vol. 7, no. 8 (Aug 2025), p. 848
Autor principal: Truong, Cong Toai
Otros Autores: Phan, Trung Dat, Duong, Van Tu, Nguyen, Huy Hung, Nguyen, Thanh Truong, Nguyen, Tan Tien
Publicado:
Springer Nature B.V.
Materias:
Pandemics
Control theory
Respiration
Oscillations
Ventilators
Control systems
Mathematical models
Algorithms
Volume controls
Optimization
Proportional derivative
Optimal control
Ventilation
Lungs
Cost effectiveness
Simulation
Low cost
Control algorithms
Bayesian analysis
Search methods
Genetic algorithms
Root locus
Objective function
Controllers
Mechanical ventilation
Tuning
Methods
Real time
Parameters
Actuators
Environmental
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Respiratory pandemics have intensified the global demand for low-cost mechanical ventilators, particularly in resource-constrained settings such as low- and middle-income countries. Numerous studies have developed simple ventilators, including bag valve mask ventilators, centrifugal blower ventilators, pneumatic ventilators, or double-acting piston pump ventilators, to prepare for future respiratory pandemics. While these ventilators share the common goal of maintaining precise air volume and pressure control, the practical development of control systems for double-acting piston pump ventilators remains under-explored. Given the complexity of developing accurate mathematical models for double-acting piston pump ventilators, this paper proposes a model-free optimization approach for controlling a double-acting piston pump used for ventilators. The method integrates a conventional proportional derivative control algorithm with Bayesian optimization to rapidly determine optimal control parameters without a precise system model and adaptively re-tune these parameters in response to fluctuations in patient respiratory conditions. Simulation results indicate that the Bayesian optimization algorithm exposes controller parameters nearly identical to those found via the grid search method, with comparable system responses. Experimental results demonstrate that the proposed algorithm significantly improves system performance, reducing both tidal volume error and control cost compared to manual tuning. Additionally, both simulation and experimental findings confirm the algorithm’s ability to automatically re-adjust controller parameters to enhance ventilation performance in response to sudden respiratory changes. The proposed control strategy aims to enhance performance while maintaining simplicity and cost-effectiveness, making it suitable for low-cost ventilators in critical healthcare environments.Article highlights<list list-type="bullet"><list-item></list-item>A Bayesian optimization-based method enables real-time tuning for volume control mode;<list-item>The objective function integrates control cost to reduce actuator oscillations;</list-item><list-item>A Root Locus-based approach constrains the search space for better stability.</list-item>
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-025-07176-x
Fuente:Science Database