IFAC Journal of Systems and Control最新文献

{"title":"Exploring variable observational time windows for patient–ventilator​ asynchrony during mechanical ventilation treatment","authors":"Christopher Yew Shuen Ang , Yeong Shiong Chiew , Xin Wang , Ean Hin Ooi , Mohd Basri Mat Nor , Matthew E. Cove , J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2024.100266","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100266","url":null,"abstract":"<div><h3>Background and Objective:</h3><p>Patient–ventilator asynchrony (PVA) is prevalent in mechanical ventilation (MV) for critically ill patients and has been associated with adverse patient outcomes. However, studies investigating the associations between PVA and patient outcomes employ differing time windows for PVA evaluation. In this study, machine learning methods are used to quantify the prevalence and magnitude of asynchrony at different time windows, as well as its temporal trends. The study aims to identify the optimal time window for assessing the temporal changes in the asynchrony index (AI) and magnitude of asynchrony (<span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>).</p></div><div><h3>Methods:</h3><p>This study uses Convolutional Neural Networks (CNN) and Convolutional Autoencoders (CAE) to detect incidences of PVA and quantify its severity in 30 MV respiratory failure patients with 2722 h of respiratory data. The frequency of PVA and the breath-average magnitude were determined over different time periods, <em>t</em>, where <span><math><mrow><mi>t</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>5</mn></mrow></math></span>, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 45, 60 min and throughout MV. The AI for the patients was determined using the CNN model. Given an asynchronous breath, the CAEs were used to reconstruct asynchrony-free waveforms. The <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span> was quantified as the difference between the two waveforms. The change in AI (<span><math><mi>Δ</mi></math></span>AI) and the change in <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>\u0000(<span><math><mrow><mi>Δ</mi><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></mrow></math></span>) for all time windows, <em>t</em> were also calculated for each patient.</p></div><div><h3>Results:</h3><p>The median [interquartile range] overall AI and <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span> for the patient cohort are 24.8 [12.9–46.1]% and 37.2 [33.4–45.3]% respectively. Analysis of the patient cohort also shows significant intra-patient variability in AI and <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>, while the inter-patient variation in AI is greater as compared to <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>. The cohort mean <span><math><mi>Δ","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"29 ","pages":"Article 100266"},"PeriodicalIF":1.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000270/pdfft?md5=094888b14f6d15c2cd2308a4be54717c&pid=1-s2.0-S2468601824000270-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141303150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrode module for EIT with a robust howland current source","authors":"Rafael B. Santos ,&nbsp;André L. Santos ,&nbsp;André C.M. Cavalheiro ,&nbsp;Rafael A.O. Ferro ,&nbsp;Fernando S. Moura ,&nbsp;Raul G. Lima ,&nbsp;Thiago C. Martins ,&nbsp;Marcos S.G. Tsuzuki","doi":"10.1016/j.ifacsc.2024.100265","DOIUrl":"10.1016/j.ifacsc.2024.100265","url":null,"abstract":"<div><p>Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs internal conductivity distributions of a body from electrical measurements taken on its boundary. This study contributes to the field by focusing on the technological intricacies of absolute EIT imaging, which is challenged by limitations such as the resolution capacity of the hardware and the complexities introduced by imaging capacitive bodies. The novel EIT system architecture proposed enhances the accuracy of measurement by integrating current sources and Analog-to-Digital Converters (ADCs) closer to the electrodes, employing alternating current excitations to accurately capture phase information. This system uses a dynamic arrangement of surface electrodes that continuously alter their roles between current injection and voltage measurement, in a synchronized sequence, to ensure the accuracy of the measurements. The paper describes the design and implementation of both the excitation and measurement subsystems, highlighting the use of digital signal demodulation near the electrode to reduce data transfer issues. Experimental results confirm the system’s capability for real-time image reconstruction at 50 frames per second with precision in phase delay measurements, suggesting significant potential for clinical and industrial applications. Future work will aim to further refine signal generation with higher-speed DACs and expand to image reconstruction with more channels.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100265"},"PeriodicalIF":1.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing and minimizing the impact of an additive disturbance for human respiratory system impedance estimation","authors":"Antoine Marchal ,&nbsp;Andy Keymolen ,&nbsp;Gerd Vandersteen ,&nbsp;Frank Heck ,&nbsp;Ben van den Elshout ,&nbsp;John Lataire","doi":"10.1016/j.ifacsc.2024.100264","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100264","url":null,"abstract":"<div><p>Respiratory Oscillometry is a promising technique to provide information to medical practitioners on the respiratory system of a patient in a non-invasive fashion. It focuses on identifying the respiratory impedance between two signals: the air pressure and flow at the mouth opening. However, for conscious patients or lightly sedated ventilated patients, their respiratory effort such as breathing acts as a disturbance to the parameter estimation procedure. This paper is an extension to previous research published at the IFAC 2023 World Congress (Marchal et al., 2023) that proposed a method to estimate and remove this breathing disturbance using Gaussian Process Regression in the frequency domain. In this extension, Monte Carlo simulations are performed to validate the approach and to compare it to the Local Polynomial Method for breathing patients. In addition, measurements carried out on a lung emulator in a pressure-support ventilation mode provide further evidence of the method’s effectiveness at dealing with the disturbance experienced for ventilated patients. This is a step towards treating both breathing and ventilated patients using the same technique.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"29 ","pages":"Article 100264"},"PeriodicalIF":1.9,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal switching for Networked Control Systems with information multiplexing","authors":"Harsh Oza ,&nbsp;Irinel-Constantin Morărescu ,&nbsp;Vineeth S. Varma ,&nbsp;Ravi Banavar","doi":"10.1016/j.ifacsc.2024.100263","DOIUrl":"10.1016/j.ifacsc.2024.100263","url":null,"abstract":"<div><p>In this article, we examine a Networked Control System (NCS) in which the plant and the controller communicate over a network subject to a certain communication constraint. The plant is described by discrete-time <em>nonlinear dynamics</em> subject to bounded disturbances. Due to an overloaded communication network, we assume that the control signal and the information from the plant (the measured output signal) cannot be transmitted simultaneously and are subject to a multiplexing constraint. The goal is to design a switching strategy that allows us to sequentially communicate given these constraints while optimizing a quadratic cost over a finite horizon. Consequently, we proceed by emulation and assume that a controller that satisfies performance requirements is already provided. The resulting optimization problem is observed to be an integer programming problem that is generally NP-complete, i.e., the complexity is exponential in the time horizon considered. To overcome this issue, we provide a different perspective on this problem than what has been presented by the community before. Our main contribution is to reformulate the problem with all its constraints to a form that renders it amenable to apply the discrete-time Pontryagin Maximum Principle to get the necessary conditions for the optimality of the control action sequence. These necessary conditions are then solved numerically by a multiple-shooting method. To validate the approach, we present some illustrative numerical experiments on an inverted pendulum. Different setups are considered and numerically analyzed: usage of a predictor when the output is not transmitted and usage of the previous value of the output when the new value is not transmitted, with or without the choice of non-transmission.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100263"},"PeriodicalIF":1.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141032159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and analyzing competitive epidemic diseases with partial and waning virus-specific and cross-immunity","authors":"Lorenzo Zino ,&nbsp;Mengbin Ye ,&nbsp;Brian D.O. Anderson","doi":"10.1016/j.ifacsc.2024.100262","DOIUrl":"10.1016/j.ifacsc.2024.100262","url":null,"abstract":"<div><p>In this paper, we consider a novel mathematical modeling framework for the spread of two competitive diseases in a well-mixed population. The proposed framework, which we term a bivirus SIRIS model, encapsulates key real-world features of natural immunity, accounting for different levels of (partial and waning) virus-specific and cross protection acquired after recovery. Formally, the proposed framework consists of a system of coupled nonlinear ordinary differential equations that builds on a classical bivirus susceptible–infected–susceptible model by means of the addition of further states to account for (temporarily) protected individuals. Through the analysis of the proposed framework and of two specializations, we offer analytical insight into how natural immunity can shape a wide range of complex emergent behaviors, including eradication of both diseases, survival of the fittest one, or even steady-state co-existence of the two diseases.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100262"},"PeriodicalIF":1.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000233/pdfft?md5=e7729cdeaef8df9cff62456b1683605d&pid=1-s2.0-S2468601824000233-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of reduced-order controllers for fluid flows using full-order controllers and Gaussian process regression","authors":"Yasuo Sasaki,&nbsp;Daisuke Tsubakino","doi":"10.1016/j.ifacsc.2024.100261","DOIUrl":"10.1016/j.ifacsc.2024.100261","url":null,"abstract":"<div><p>We propose a method to design reduced-order output-feedback controllers for fluid flows with the use of data produced by full-order controllers. First, the full-order controller is obtained by combining an ensemble Kalman filter (EnKF) and a model predictive controller (MPC) that are designed based on the Navier–Stokes equations. The full-order controller has high computational complexity and, therefore, is not suitable for real-time implementation. Hence, we use the full-order controller in offline numerical simulations to generate data for data-driven design of the reduced-order controller with low computational complexity. We find a reduced-order subspace of a closed-loop system under the full-order control from the data. This subspace underlies the reduced-order output-feedback controller. The reduced-order state-feedback law is obtained by approximating the full-order MPC with the use of its input/output data. The reduced-order observer is designed for a reduced-order model that is derived by using the Gaussian process regression (GPR). The GPR enables us to design the reduced-order observer which can evaluate uncertainty due to state-dependent residuals of the reduced-order model. We demonstrate the proposed method for a control problem of a flow around a cylinder at the Reynolds number 100. Numerical simulations reveal that the reduced-order controller performs as almost well as the full-order controller for a set of initial states. In addition, robustness of the reduced-order controller to a temporal disturbance that is not considered in the control design is confirmed in the simulations.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100261"},"PeriodicalIF":1.9,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000221/pdfft?md5=93952d30a9a532b3d7b463feb519c294&pid=1-s2.0-S2468601824000221-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140761630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature space separation by conformity loss driven training of CNN","authors":"N. Ding ,&nbsp;H. Arabian ,&nbsp;K. Möller","doi":"10.1016/j.ifacsc.2024.100260","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100260","url":null,"abstract":"<div><p>Convolutional neural networks (CNNs) have enabled tremendous achievements in image classification, as the model can automatically extract image features and assign a proper classification. Nevertheless, the classification is lacking robustness to — for humans’ invisible perturbations on the input. To improve the robustness of the CNN model, it is necessary to understand the decision-making procedure of CNN models. By inspecting the learned feature space, we found that the classification regions are not always clearly separated by the CNN model. The overlap of classification regions increases the possibility to less perturbation induced input changes on classification results. Therefore, the clear separation of feature spaces of the CNN model should support decision robustness. In this paper, we propose to use a novel loss function called “conformity loss” to strengthen disjoint feature spaces during learning at different layers of the CNN, in order to improve the intra-class compactness and inter-class differences in trained representations. The same function was used as an evaluation metric to measure the feature space separation during the testing process. In conclusion, the conformity loss driven trained model has shown better feature space separation at comparable output performance.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100260"},"PeriodicalIF":1.9,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246860182400021X/pdfft?md5=7ae999412c5f76db07310209ce438ec2&pid=1-s2.0-S246860182400021X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating patient spontaneous breathing effort in mechanical ventilation using a b-splines function approach","authors":"Qianhui Sun ,&nbsp;J. Geoffrey Chase ,&nbsp;Cong Zhou ,&nbsp;Merryn H. Tawhai ,&nbsp;Jennifer L. Knopp ,&nbsp;Knut Möller ,&nbsp;Geoffrey M. Shaw ,&nbsp;Thomas Desaive","doi":"10.1016/j.ifacsc.2024.100259","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100259","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background:&lt;/h3&gt;&lt;p&gt;Patient work of breathing is a key clinical metric strongly to guide patient care and weaning from mechanical ventilation (MV). Measurement requires added equipment, well-trained clinicians, or/and extra interventions. This study combines a spontaneous breathing effort model using b-spline functions with a nonlinear, predictive MV digital-twin model to monitor patient effort in real-time.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;p&gt;Data from 22 patients for two assisted spontaneous breathing MV modes, NAVA (neurally adjusted ventilatory assist) and PSV (pressure support ventilation), are employed. The patient effort function estimates a pleural pressure &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; surrogate of muscular work of breathing induced pressure. To ensure identifiability &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is identified with a negative constraint level of 75%. Estimated patient effort is compared to electrical activity of the diaphragm (EAdi) signals from the NAVA naso-gastric tude, airway pressure, and tidal volume (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) as well as physiological and clinical expectations.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;p&gt;&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; generalizes well across the digital twin model and MV modes in comparison to the original single compartment lung model. Strong neuro-muscular correlations are identified with &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; compared to EAdi, &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, and airway pressure in NAVA. They are lower in PSV, as expected, as pressure delivery is not a function of EAdi in this MV mode, while the uncontrolled variable &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; shows a stronger association with &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; than EAdi.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;p&gt;The digital twin model relates patient-specific induced breathing effort, modeled as &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;ˆ&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, as well as or better than EAdi in both assisted breathing MV modes. Results differ between NAVA and PSV modes due to the poorer patient–ventilator interaction typical in PSV. The ability to estimate patient work of breathing allows non-invasive, real-time quantification of ventilator unloading, heretofore not possible without ex","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"28 ","pages":"Article 100259"},"PeriodicalIF":1.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140618985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controllers and observer synthesis for linear systems with multiple time-varying delays in range","authors":"S. Syafiie","doi":"10.1016/j.ifacsc.2024.100257","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100257","url":null,"abstract":"<div><p>Most of physical systems present time-varying delays in their inner dynamics. This causes instability, oscillation and even poor closed performance. Also, the present disturbance can cause instability. This article is addressing techniques to develop stability criteria for closed-loop and states estimation analysis of multiple time-varying delays systems. By selecting a suitable Lyapunov–Krasovskii functional (LKF), the derivative of double integration terms are upper bounded by using reciprocally convex matrix inequality. The closed-loop stability criteria are derived fulfilling <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance index for multiple time-varying delays systems. Similar technique is also adopted to estimate unmeasured states fulfilling <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> norm bound. The developed criteria are demonstrated to a numerical example. It is shown that H<span><math><msub><mrow></mrow><mrow><mi>∞</mi></mrow></msub></math></span> memory based controller has better performance on rejecting the introduction disturbance with having lower peak and shallow valley than other techniques.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100257"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-competitive time-varying networked SIS model with an infrastructure network","authors":"Sebin Gracy ,&nbsp;José I. Caiza ,&nbsp;Philip E. Paré ,&nbsp;César A. Uribe","doi":"10.1016/j.ifacsc.2024.100254","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100254","url":null,"abstract":"<div><p>The paper studies the problem of the spread of multi-competitive viruses across a (time-varying) population network and an infrastructure network. To this end, we devise a variant of the classic (networked) susceptible–infected-susceptible (SIS) model called the multi-competitive time-varying networked susceptible-infected-water-susceptible (SIWS) model. We establish a sufficient condition for exponentially fast eradication of a virus when a) the graph structure does not change over time; b) the graph structure possibly changes with time, interactions between individuals are symmetric, and all individuals have the same healing and infection rate; and c) the graph is directed and is slowly-varying, and not all individuals necessarily have the same healing and infection rates. We also show that the aforementioned conditions for eradication of a virus are robust to variations in the graph structure of the population network provided the variations are not too large. For the case of time-invariant graphs, we give a lower bound on the number of equilibria that our system possesses. Finally, we provide an in-depth set of simulations that not only illustrate the theoretical findings of this paper but also provide insights into the endemic behavior for the case of time-varying graphs.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100254"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140209091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}