Precise oxygen therapy to emphysema patients by fuzzy-based gain tuning control of set-point regulated MRAC

Highlights

This research outlines an automated oxygen delivery system for emphysema patients:

• System Complexity: Developed a complex mathematical model for the human respiratory system and accommodated oxygen exchange delays typical in emphysema.
• The SFMRAC Model: Proposes an intelligent controller combining fuzzy tuning and set-point modulation for better accuracy.
• Adaptability: Enhances standard adaptive control with normalization to respond precisely to real-time physiological changes.
• Clinical Goal: Provides a knowledge-based, automatic system to stabilize oxygen levels and reduce patient suffering without manual intervention.

Abstract

Emphysema, a primary component of chronic obstructive pulmonary disease (COPD), causes progressive dyspnea through the destruction of alveolar membranes. This structural degradation reduces the available surface area for gas exchange, significantly impairing oxygen delivery to the bloodstream. While oxygen therapy is a critical intervention, the inherent physiological complexities, specifically transit time delays and dynamic respiratory demands, make precise oxygen regulation exceptionally difficult. To address these challenges, this study develops a comprehensive mathematical model of the emphysema-affected respiratory system, incorporating specific parameters for time delays in oxygen exchange. A novel Intelligent Set-point Modulated Fuzzy Model Reference Adaptive Controller (SFMRAC) is proposed to enhance oxygen regulation. This control architecture advances traditional Model Reference Adaptive Controller (MRAC) by integrating a normalization factor, fuzzy logic tuning, and set-point modulation. This hybrid approach allows the system to adapt to nonlinear physiological variations and maintain stability despite the transit delays characteristic of damaged pulmonary tissue. The effectiveness of the SFMRAC was evaluated through a simulation study conducted in MATLAB/Simulink. Results demonstrate that the proposed controller provides superior tracking performance and robustness compared to MRAC, particularly when subjected to varying set-points and significant exchange delays. The results suggest that the SFMRAC offers a promising computational framework to improve the automated delivery of oxygen therapy in clinical settings for COPD patients.

Introduction

One of the most frustrating and challenging human conditions is breathing issues. Asthma, pulmonary fibrosis, bronchitis, emphysema, and chronic obstructive pulmonary disease (COPD), as well as various critical care conditions, can cause these problems [1]. The regulation of arterial oxygen saturation (SpO₂) is a vital component in the treatment of COPD, particularly emphysema. Smoking is the primary cause of this illness. Long-term smoking causes the alveolar sacs to expand, eventually leading to their rupture and the development of large air pockets in the lungs [2,3]. When oxygen exchange is impeded by lung injury and alveolar breakdown, the immediate outcome is breathlessness. Breathing becomes difficult, restricting oxygen transport to the red blood cells, and resulting in oxygen deficiency in internal organs. In these circumstances, the body's oxygen shortage, potential organ failure, and weakened immune responses worsen the crisis [4]. In clinical settings, supplemental oxygen is frequently administered to maintain SpO₂ levels within a therapeutic window (typically around 95%). However, the higher-order dynamics and nonlinearities of the human respiratory system make manual titration of oxygen flow difficult and prone to human error.

Effective automated regulation requires a controller that can adapt to inter-patient variability and the shifting pathological states caused by alveolar infections. Numerous studies have demonstrated that long-term oxygen therapy has increased the quality and duration of life for persons with chronic lung disease. Simple on/off control over oxygen delivery can be used to adjust the dosage [5,6]. A more complex and sophisticated control scheme, such as PID (Proportional–Integral–Derivative) controller and fuzzy logic control (FLC), which uses the difference between the patient's actual blood oxygen content and a target blood oxygen content and/or trends in the blood oxygen content, suggested that the amount of oxygen delivered to the patient with each inhalation could be adjusted according to the patient's need. By automatically regulating the oxygen therapy dosage, another technique can safeguard and administer the oxygen supply to patients during respiratory system disorders [7]. Traditional, or proportional-integral-derivative (PID) control methods need precise quantitative data and mathematical representations of the system that has to be regulated to adjust the controller's settings. PID controllers lack the adaptive capacity to handle these fluctuations and often result in excessive overshoot, which can lead to fatal oxygen toxicity. While MRAC offers a theoretical solution by forcing the patient's response to follow a stable reference model, its practical application is hindered by the sensitivity and complexity of tuning its adaptation gain factors. This is where fuzzy logic control, or FLC, comes in handy. An expert-knowledge-based language control approach can be transformed into an automatic control strategy using the FLC.

For mechanical ventilation processes, Mehedi, Ibrahim et al. [6] put in place a fuzzy PID controller. Due to the large number of control parameters involved, tuning a fuzzy controller is a challenging operation. However, Mehedi, Ibrahim et al. [6] neglected to adjust the controller, which is crucial for vital functions like the respiratory system in humans. The O2matic®-based closed-loop regulated oxygen supply was proposed by Hansen et al. [7] during the COVID pandemic. It includes flow rate restrictions and modifies flow every second based on a 15-s average of the SpO₂ value. The Adaptive Fuzzy PD Controller (AFLPDC) and the Set-point Modulated Fuzzy PI Model Reference Adaptive Controller (SFPIMRAC) are proposed as solutions to this emergency problem, to control the oxygen levels of emphysema patients without requiring human intervention [[8], [9], [10]]. However, the overshoot problem, MRAC input variations, and change in adaptation gain were not fully eliminated by SFPIMRAC [10], and as a result, process instability may occasionally occur. To solve these issues, the MIT rule and the normalized algorithm are incorporated into the controller design in this study. This study integrates fuzzy tuning and set-point modulation to improve the performance of the model reference adaptive controller (MRAC). It suggests an intelligent set-point modulated fuzzy model reference adaptive controller (SFMRAC) to improve oxygen supply regulation in emphysematous patients.

The subsequent sections of this paper detail the development of the ninth-order respiratory model, the design of the fuzzy-based adaptation mechanism, and a comprehensive comparative analysis demonstrating the superiority of SFMRAC over conventional control strategies in terms of safety, speed, and robustness.