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Improving Flow Control in Electro-pneumatic Lung Ventilators: PID versus Fuzzy Logic Control Systems. Partial Results
Abstract
This study aims to evaluate the implementation of artificial intelligence, specifically fuzzy logic, in the flow control system of a mechanical pulmonary ventilator. The research compares the performance of the fuzzy logic control system with the conventional PID control system. A test bench was constructed to test and analyze the control algorithms on an electro-pneumatic ventilator, with the PID control algorithm initially applied and the test lung parameters adjusted under various conditions. The results demonstrated that the PID controller algorithm is effective and precise within the target flow range set by the user. However, the fuzzy logic algorithm is anticipated to significantly enhance these results due to its sensitivity to small changes, reliability, and improved accuracy. This research contributes to the understanding and improvement of flow control systems in electro-pneumatic lung ventilators, paving the way for enhanced patient care and treatment outcomes.
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The Biological systems are highly non-linear, and there is a significant amount of variability in the results from one patient to another. It is observed that High Blood Pressure is a major health issue in today's World. The human body, particularly the cardiovascular parameters are significantly ill impacted by longer bed rest. Using Fuzzy controller and Fuzzy logic helps to overcome this negative impact and to enhance functional recovery. The nature of the cardio-vascular system is time-variant and non-linear. Here a self-learning adaptive fuzzy controller is used. This fuzzy controller has an intelligent structure and does not require any prior knowledge about the patient and about the range of cardiovascular parameters. This fuzzy controller manages the body's inclination for adjusting the blood pressure and heart rate as the inclination can increase and decrease the cardiovascular parameters. This helps to maintain the cardiovascular parameters which can enhance the recovery of the patient and make the patient active in lesser time. Here the goal is to mobilize the patients faster maintaining their blood pressure and heart rate using fuzzy logic.
Se entiende por lógica difusa la forma de representar matemáticamente, razonamientos e ideas imprecisas o aproximadas. Se basa en relaciones de entrada-salida representadas en un compendio de reglas difusas, que son expresiones lingüísticas que asocian una causa con un efecto. Su versatilidad la ha hecho apta para la solución de problemas de seguimiento de referencia en ingeniería de control, donde ha mejorado el desempeño de controladores para sistemas no necesariamente lineales e invariantes en el tiempo. En su teoría, se estudian distintos esquemas de control en los cuales la lógica difusa desempeña un papel determinante en su condición de control adaptivo. También, la capacidad que tienen los sistemas de inferencia difusa (SID) para la identificación de sistemas dinámicos, aporta soluciones a esquemas de control que contemplan modelos de referencia. Así, las técnicas de lógica difusa en ingeniería de control han sido un alternativa exitosa en los esfuerzos dirigidos a mejorar el rendimiento de sistemas de control ante no linealidades, variabilidad de parámetros y situaciones en las cuales la información del proceso a controlar es impreciso o poco conocido.
Respiratory diseases are one of the most common causes of death in the world and this recent COVID-19 pandemic is a key example. Problems such as infections, in general, affect many people and depending on the form of transmission they can spread throughout the world and weaken thousands of people. Two examples are severe acute respiratory syndrome and the recent coronavirus disease. These diseases have mild and severe forms, in which patients gravely affected need ventilatory support. The equipment that serves as a basis for operation of the mechanical ventilator is the air–oxygen blender, responsible for carrying out the air–oxygen mixture in the proper proportions ensuring constant supply. New blender models are described in the literature together with applications of control techniques, such as Proportional, Integrative and Derivative (PID); Fuzzy; and Adaptive. The results obtained from the literature show a significant improvement in patient care when using automatic controls instead of manual adjustment, increasing the safety and accuracy of the treatment. This study presents a deep review of the state of the art in air–oxygen benders, identifies the most relevant characteristics, performs a comparison study considering the most relevant available solutions, and identifies open research directions in the topic
A personalized mechanical ventilation approach for patients with adult respiratory distress syndrome (ARDS) based on lung physiology and morphology, ARDS etiology, lung imaging, and biological phenotypes may improve ventilation practice and outcome. However, additional research is warranted before personalized mechanical ventilation strategies can be applied at the bedside. Ventilatory parameters should be titrated based on close monitoring of targeted physiologic variables and individualized goals. Although low tidal volume ( V T ) is a standard of care, further individualization of V T may necessitate the evaluation of lung volume reserve (e.g., inspiratory capacity). Low driving pressures provide a target for clinicians to adjust V T and possibly to optimize positive end-expiratory pressure (PEEP), while maintaining plateau pressures below safety thresholds. Esophageal pressure monitoring allows estimation of transpulmonary pressure, but its use requires technical skill and correct physiologic interpretation for clinical application at the bedside. Mechanical power considers ventilatory parameters as a whole in the optimization of ventilation setting, but further studies are necessary to assess its clinical relevance. The identification of recruitability in patients with ARDS is essential to titrate and individualize PEEP. To define gas-exchange targets for individual patients, clinicians should consider issues related to oxygen transport and dead space. In this review, we discuss the rationale for personalized approaches to mechanical ventilation for patients with ARDS, the role of lung imaging, phenotype identification, physiologically based individualized approaches to ventilation, and a future research agenda.
This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient’s hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.
When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.
Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.
Solar trackers represent an essential tool to increase the energy production of photovoltaic modules compared to fixed systems. Unlike previous technologies where the aim is to keep the solar rays perpendicular to the surface of the module and obtain a constant output power, this paper proposes the design and evaluation of two controllers for a two-axis solar tracker, which maintains the power that is produced by photovoltaic modules at their nominal value. To achieve this, mathematical models of the dynamics of the sun, the solar energy obtained on the Earth’s surface, the two-axis tracking system in its electrical and mechanical parts, and the solar cell are developed and simulated. Two controllers are designed to be evaluated in the solar tracking system, one Proportional-Integral-Derivative and the other by Fuzzy Logic. The evaluation of the simulations shows a better performance of the controller by Fuzzy Logic; this is because it presents a shorter stabilization time, a transient of smaller amplitude, and a lower percentage of error in steady-state. The principle of operation of the solar tracking system is to promote the orientation conditions of the photovoltaic module to generate the maximum available power until reaching the nominal one. This is possible because it has a gyroscope on the surface of the module that determines its position with respect to the hour angle and altitude of the sun; a data acquisition card is developed to implement voltage and current sensors, which measure the output power it produces from the photovoltaic module throughout the day and under any weather conditions. The results of the implementation demonstrate that a Fuzzy Logic control for a two-axis solar tracker maintains the output power of the photovoltaic module at its nominal parameters during peak sun hours.
Mechanical ventilation is an important and effective method for the treatment of pulmonary diseases patients with spontaneous breathing. Spontaneous breathing refers to the physiological breathing activity caused by the respiratory muscle. These patients retain some ability to breathe spontaneously, but do not reach the level of normal breathing. Mathematical simulation and modeling of the mechanical ventilation system are crucial for research on mechanical ventilation. In this paper, a novel pneumatic model of a mechanical ventilation system considering patients' spontaneous breathing is presented. Mathematical equations are accurately derived to explain the principles of the respiratory system and mechanical ventilation system. An experimental prototype is designed to confirm the correctness and validity of the pneumatic model. The goodness of fit shows that the mathematical simulation curve fits well with the experimental curve, thus confirming the accuracy of the pneumatic model. For patients with a certain degree of spontaneous breathing, the mechanical ventilation mode is set to the pressure support ventilation (PSV) mode, and variations in the flow, pressure and tidal volume curves are observed by changing specific respiratory mechanics parameters such as the compliance (C), the effective area of the throttle in the pneumatic model (A), and the muscle pressure difference (ΔPmus). From the results, it can be concluded that the resistance of the mechanical ventilation system can be equivalent to A. The dynamic characteristics (mainly flow characteristics, tidal volume characteristics and pressure characteristics) of the mechanical ventilation system are directly influenced by variations in C, A and ΔPmus. This study is an important reference for setting ventilation levels and ventilator control parameters. The results of this research are valuable for the diagnosis and treatment of respiratory diseases.
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.
In this paper, we introduce a variable-gain control strategy for mechanical ventilators in the respiratory systems. Respiratory systems assist the patients who have difficulty breathing on their own. For the comfort of the patient, fast pressure buildup (and release) and a stable flow response are desired. However, linear controllers typically need to balance between these conflicting objectives. In order to balance this tradeoff in a more desirable manner, a variable-gain controller is proposed, which switches the controller gain based on the magnitude of the patient flow. The effectiveness of the control strategy is demonstrated in experiments on different test lungs.
Proportional integral derivative controllers are widely used in industrial processes because of their simplicity and effectiveness for linear and nonlinear systems. The fuzzy controller is the most suitable for the human decision-making mechanism, providing the operation of an electronic system with decisions of experts. In addition, using the fuzzy controller for a nonlinear system allows for a reduction of uncertain effects in the system control. In this study, a proportional integral derivative controller and a fuzzy logic controller are designed and compared for a single-axis solar tracking system using an Atmel microcontroller. According to the angle of solar energy, a solar panel is oriented to the side where light intensity is greatest by being designed for the related supervisory controllers. Thus, the aim is to increase the energy obtained from solar panels by providing the specular reflection of the sun’s rays to a solar panel. At the same time, a maximum efficient processing system has been determined by taking account of two controllers for the designed system.
En este artículo de investigación científica y desarrollo tecnológico, se presenta el diseño de un sistema de control
de autorregulación de oxígeno, mediante lógica difusa, que permite suministrar a un paciente una cantidad exacta
de la fracción inspirada de oxígeno FIO2 teniendo en cuenta los niveles de saturación de oxígeno. Para tal efecto,
se realizó el modelado matemático de la curva de disociación de hemoglobina a partir de datos obtenidos por
oximetría que relacionan la saturación de oxígeno O2 y la presión de oxígeno PAO2. Posteriormente, se realizó el
modelado de una válvula proporcional para aplicaciones médicas, la cual recibe una señal de corriente obtenida a
partir de la mezcla de aire y oxígeno. Finalmente se diseñó el controlador difuso tipo Sugeno con una entrada, una
salida para la apertura de la válvula y once reglas difusas definidas a partir de la entrevista con especialistas. De
esta forma se realizó una investigación del tipo factible soportada en un diseño de campo y en uno documental.
Los resultados obtenidos demostraron la efectividad del controlador difuso para mantener la válvula en un nivel
exacto de FIO2 compuesto por la mezcla de aire y oxígeno.
The knowledge in this paper describe methods and systems for supplying supplemental oxygen to patients for use in sub-acute respiratory illnesses which maintains healthy blood oxygen content in the patients by controlled dosing of oxygen with a measured response to the patient's actual blood oxygen content are disclosed. The dosing can be provided by simple ON/OFF control over the delivery of oxygen or the amount of oxygen delivered to the patient with each inhalation can be varied in response to the patient's need as determined by a more sophisticated control scheme, such as PID (proportional-integral-derivative) controller and Fuzzy logic control (FLC) that utilizes the difference between the patient's actual blood oxygen content and a target blood oxygen content and/or trends in the blood oxygen content. The systems and methods are particularly directed at patients receiving supplemental oxygen therapy in a sub-acute care environment. The result of the two controllers is good and best one is the fuzzy logic algorithm.
In the Ziegler-Nichols's method of reaction curve, the proportional gain should be calculated as an inverse relation of the plant steady-state gain. One of the reasons behind this is to avoid an excessively high loop gain, which can jeopardize many required characteristics of the closed loop. However, many reports, scientific papers and books have been neglecting such gain compensation in the tuning formulae. This brief presents a comprehensive discussion about such uncompensated tuning rules. The main paper finding is that either the stability margin or the disturbance rejection is reduced in this case. A theoretical analysis is performed to obtain the main result. Moreover, a consistent simulation study is also performed to show the impact of the lack of compensation on performance.
Automatic control of blood pressure after cardiac operation of patient is wanted in favor of enhanced patient concern; it decreases work of personnel and expenses. Automation of medical drug infusion for controlling of mean arterial pressure (MAP) is extremely advantageous in much clinical function. An assimilating self-tuning control approach for the regulation of mean arterial pressure by infusing sodium nitroprusside is discussed. This paper focuses on omnipresent and verified FUZZY controllers based on reinforcement learning for arterial blood pressure control. The major problem is patient’s sensitivity in different condition although is same condition at different time. To extract the patient’s parameter reinforcement learning approach is proposed and verified. Complete & convenient model of hypertensive patient is effectively developed and processed; with drug response model depiction. Intend and execution of such control arrangement will be controlled using FUZZY logic controllers and for parameter extraction deterministic learning is used. MATLAB Simulation of the designed system models are done for revelation.
Lógica Difusa Aplicada al Control Local del Péndulo Invertido con Rueda de Reacción
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VT650 Gas Flow Analyzer Ventilator Tester
- Fluke Biomedical
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