IFAC Journal of Systems and Control最新文献

{"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":"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}

{"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":"Automatic control of reactive brain computer interfaces","authors":"Pex Tufvesson ,&nbsp;Frida Heskebeck","doi":"10.1016/j.ifacsc.2024.100251","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100251","url":null,"abstract":"<div><p>This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100251"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000129/pdfft?md5=121b6676b9693cead323163b827332bb&pid=1-s2.0-S2468601824000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042122","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":"Ubiquity of models describing inspiratory effort dynamics in patients on pressure support ventilation","authors":"Jennifer L. Knopp ,&nbsp;Yeong Shiong Chiew ,&nbsp;Dimitrios Georgopoulos ,&nbsp;Geoffrey M. Shaw ,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2024.100250","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100250","url":null,"abstract":"<div><p>Mechanical Ventilation (MV) is an important therapy in the intensive care unit (ICU). Assisted spontaneous breathing (ASB) MV modes are a key and growing part of MV care, as they require less sedation and help avoid muscle atrophy. Equally, a lack of standardised approaches to MV care has led to the rise of model-based methods, which typically cannot estimate spontaneous breathing (SB) efforts, and are thus not able to be used for ASB MV. To address this issue, several models of SB effort have been created, though they require specialised added sensors and/or maneuvers. ►This research utilises a unique observational clinical dataset, which includes esophageal and gastric pressure measurements not typically taken in the ICU for N=6 patients. These measurements enable more direct model-based estimation of muscular pressure effort in SB MV patients. The data is analysed for all 6 patients for 3 models which include dynamics common to the current models. Models are assessed based on model fit. ►The results show all 3 models are unable to capture dynamics in 2 patients due to added muscular dynamics in their breathing, violating assumptions in the model dynamics or constraints. These results indicate the need for more flexible models and associated identification methods to better capture these dynamics.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100250"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140024358","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":"Reliable H∞ fuzzy control for fault-tolerant actuator failures of active suspension system","authors":"Jamal Mrazgua ,&nbsp;El Houssaine Tissir ,&nbsp;Mohamed Ouahi ,&nbsp;Fernando Tadeo","doi":"10.1016/j.ifacsc.2024.100258","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100258","url":null,"abstract":"<div><p>A new methodology for fault tolerant control (FTC) is proposed to compensate actuator failures using Takagi–Sugeno systems. This makes possible to design the controller that represents actuator failures using a scaling factor by solving a family of linear matrix inequalities (LMIs). The resulting control system guarantees asymptotic stability, compensates the effect of actuator faults and ensures certain an <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance level. This methodology is applied to the active suspension systems that motivated this research, where in the context of active suspension (AS) systems, the guaranteed performance correspond to ride comfort in the presence of road disturbances. Thus, a controller is developed for a quarter-car model with active suspension. The simulated results illustrate the effectiveness of the proposed approach.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100258"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549185","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":"Novel Human Activity Recognition by graph engineered ensemble deep learning model","authors":"Mamta Ghalan,&nbsp;Rajesh Kumar Aggarwal","doi":"10.1016/j.ifacsc.2024.100253","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100253","url":null,"abstract":"<div><p>This research delves into the domain of Human Activity Recognition (HAR) through sensor data analysis, offering a comprehensive exploration of three diverse datasets: UniMiB-SHAR, Motion Sense, and WISDM Actitracker. The UniMiB-SHAR dataset encompasses a diverse array of linear as well as non-linear and complex activities which involve the movement of more than one joint or muscle (for example Hitting Obstacles, jogging and falling with face down). This motion generates highly correlated sensor readings over a certain period of time. In this case, Convolution Neural Networks (CNNs) are effective in feature extraction as well as classification of HAR activities, but they may not fully grasp the combined features of spatial as well as temporal aspects in the HAR datasets and heavily rely on labelled data. Whereas, Graph convolution networks (GCN), with their capacity to model complex interactions through graph structure, complement CNN’s capabilities in classifying non-linear activities in the HAR dataset. By leveraging the Knowledge graph structure and acquiring the feature embeddings from the GCN model, in this study, a Noval ensemble CNN model is proposed for the classification of activities. The novel HAR pipeline is termed as Graph Engineered EnsemCNN HAR (GE-EnsemCNN-HAR) and its performance is evaluated on HAR datasets. Proposed model demonstrated a noteworthy accuracy of 93.5% on UniMiB-SHAR dataset, surpassing the Shallow CNN model with GNN with an improvement of 20.14%. The proposed model achieved a notable accuracy rate of 96.18% and 98% when evaluated on the Motion Sense and WISDM Actitracker dataset.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100253"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180271","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":"Novel adaptive observer for HVDC transmission line: A new power management approach for renewable energy sources involving Vienna rectifier","authors":"Adil Mansouri ,&nbsp;Abdelmounime El Magri ,&nbsp;Rachid Lajouad ,&nbsp;Fouad Giri","doi":"10.1016/j.ifacsc.2024.100255","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100255","url":null,"abstract":"<div><p>This paper proposes a novel approach to control and manage energy production based on grid requirements. The main challenge is the distance between the load and production area, making it difficult to quantify energy requirements in real time at the production area. To overcome this challenge, the proposed approach relies on an adaptive observer design that provides accurate and reliable estimates of multiple signals without expensive and unreliable sensors. In this study, a renewable energy source that consists of a wind turbine coupled to a permanent magnet synchronous generator is actuated with a Vienna power converter. However, it is crucial to emphasize that this approach can be implemented at any production site, regardless of its nature. The paper’s main contribution is the design of a novel adaptive high-gain observer that estimates the grid energy requirement based on the voltage value at the endpoint of the high-voltage direct current line. Moreover, the system load parameters are unknown and come non-linear in the system model. Simulations and analysis demonstrate the effectiveness of the proposed observer, showing convergence to the origin under the well-established condition of persistent excitation (PE).</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100255"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141847","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}