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SSRN 2025年

作者： Bronczyk, Karolina Dąbrowska, Agata Dąbrowski, Adam Konieczka, Adam Department of Trace Analysis Faculty of Chemistry Adam Mickiewicz University Uniwersytetu Poznanskiego 8 Poznan61-614 Poland Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3A Poznan60-965 Poland

Food contact materials (FCMs) may be responsible for migration of contaminants into food, especially during heating. This article describes the effect of electromagnetic microwaves used to heat food on the migration of organic contaminants into neutral, and acidic food. An untargeted analysis (GC-MS) was performed to determine groups of organic compounds, which can be released most quickly from FCMs into heated food. Low-molecular-weight carbonyl compounds are particularly important migrants, as they can affect the quality and sensory properties of foods. It makes them suitable for use as markers in migration studies. A targeted analysis was performed to determine the factors determining the intensity of migration processes. The derivatization method using the reagent 2,3,4,5,6-pentafluorobenzylhydroxylamine (PFBOA) was used, which allows the use of gas chromatography coupled with an electron capture detector (GC-ECD). The study showed that plant-based FCMs lead to greater contamination of food with undesirable carbonyls than glass and plastics. Short-term storage of food in a refrigerator promotes the migration of carbonyls to food. Longer storage reduces the total concentration of carbonyls in neutral food. However, aldehydes and ketones are more biologically stable in acidic food. Additionally, plant-based FCMs can affect the sensory profile of food heated briefly with the microwave radiation. © 2025, The Authors. All rights reserved.

关键词： Acetone

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A meta-reinforcement learning method for adaptive payload transportation with variations

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Neurocomputing 2025年 638卷

作者： Chen, Jingyu Ma, Ruidong Xu, Meng Candan, Fethi Mihaylova, Lyudmila Oyekan, John Institute of Software Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Information Technology & Management University of International Business and Economics Beijing China Department of Computer Science University of York York United Kingdom

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV–payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV–payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://***/view/meta-payload-fly/ and the source code is available at https://***/wawachen/Meta-load-fly. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Integrated design of system structure and delayed resonator towards efficient non-collocated vibration absorption

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Journal of Sound and Vibration 2025年 612卷

作者： Peichl, Adam Kuře, Matěj Michiels, Wim Vyhlídal, Tomáš Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 4 Prague 6166 07 Czech Republic Department of Computer Science KU Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium

The problem of non-collocated vibration absorption by a delayed resonator is addressed with emphasis on system fatigue resistance and energy efficiency of control actions. The analysis is performed for a system consisting of an arbitrary large series of flexibly linked single-degree-of-freedom masses. For the stage where the vibration of the target mass is fully absorbed by the non-collocated resonator, key forces, motion amplitudes and potential energies across the system structure are assessed. Next, a complete parameter set of the resonator gain and delay is derived, and the actuation force and power needed by the resonator for the full vibration absorption is determined. The derived quantities are utilized in forming an optimization problem to balance minimal risk of fatigue across the system structure and power needed by the resonator, under the closed loop stability and parameter constraints. Next to the gain and delay of the resonator, selected structural parameters of the system are used as variables in the constrained nonlinear optimization problem. Experimental and numerical case studies are included to demonstrate benefits of the proposed integrated structural and control design. © 2025 The Authors

关键词： Structural health monitoring

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A robust energy flow predictor based on CNN-LSTM for prosumer-oriented microgrids considering changes in biogas generation

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Energy 2025年 326卷

作者： Maślak, Grzegorz Orlowski, Przemyslaw West Pomeranian University of Technology in Szczecin Department of Automatic Control and Robotics Faculty of Electrical Engineering Sikorskiego 37 Szczecin70-313 Poland West Pomeranian University of Technology in Szczecin Doctoral School Piastów 19 Szczecin70-310 Poland

Information, such as reliable demand and generation forecasts is crucial for appropriate energy management in microgrids. However, the most valuable data is the aggregate energy flow in the managed network, rather than its components. The large share of renewable energy in such installations is associated with fluctuations. A promising renewable source that helps counteract them is biogas. However, a source subject to uncertainties which should be taken into account. The work focuses on developing a method for obtaining a CNN-LSTM microgrid energy flow predictor dedicated to energy management systems. The selection of the hyper-parameters of the artificial neural network is formulated as a robust meta-optimisation problem and solved using surrogate optimisation. The solution ensures obtaining a CNN-LSTM network providing the lowest prediction error with the largest change in the share of biogas in the microgrid. Detailed analyses have shown that in addition to obtaining high robustness to biogas, the solution does not lose accuracy while changing the number of prosumers with photovoltaic installations. Beyond meta-optimisation, ranges of certain hyper-parameters providing adequate prediction quality for all analysed prediction horizons are identified. © 2025 Elsevier Ltd

关键词： Information management

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H observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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Event-Triggered Observer-Based H_∞ Optimal Tracking Formation control for ITSs With Longitudinal-Lateral Slips and Disturbances

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Tan, Luy Nguyen Ho Chi Minh City University of Technology Vietnam National University Department of Automatic Control Faculty of Electrical-Electronics Engineering Ho Chi Minh City 700000 Viet Nam

Distributed control has been extensively extended to networked intelligent transport systems (ITSs), addressing challenges such as unknown internal dynamics, output feedback, external disturbances, and dead-zone inputs. However, existing studies have not considered longitudinal-lateral slip dynamics, which affect yaw angle measurements due to general disturbances rather than Gaussian noise. This paper, therefore, aims to design an H_∞ optimal tracking formation control scheme resisting the unfavorable factors. Firstly, the event-triggered (ET) observers are proposed to estimate unavailable states and measurement disturbances, simultaneously such that the parameters are adjusted only when a triggering condition is violated. Secondly, a robust optimal control algorithm is built based on the development of a three-player zero-sum game theory and the integral reinforcement learning principle, where the control input player cooperates with the dead-zone compensation player to minimize a L₂-gain consensus cost function while the external disturbance player tries to maximize it. The algorithm guarantees that the compensation laws of the dead-zone disturbance are approximated, and the largest unknown upper bounds to reject external disturbances are determined. In addition, the Zeno phenomenon and the persistence of excitation are excluded, and errors are uniformly ultimately bounded. Finally, the utilities of the observers and controllers are comparatively validated by the numerical simulation results of a networked ITS group. © 2000-2011 IEEE.

关键词： event-triggering mechanism game theory integral reinforcement learning ITS tracking formation control

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Cerebral ischemia detection using deep learning techniques

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Health Information Science and Systems 2025年第1期13卷 1-19页

作者： Pastor-Vargas, Rafael Antón-Munárriz, Cristina Haut, Juan M. Robles-Gómez, Antonio Paoletti, Mercedes E. Benítez-Andrades, José Alberto Communications and Control Systems Computer Engineering Science Faculty UNED Calle Juan del Rosal 16 Madrid 28040 Spain Radiology area Hospital Universitario de Navarra Navarra Spain School of Technology University of Extremadura Extremadura Cáceres Spain ALBA Research Group Department of Electric Systems and Automatics Engineering University of Leon Campus of Vegazana s/n Castilla y León León 24071 Spain

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns. © The Author(s) 2025.

关键词： Cerebral ischemia Computed tomography Deep learning Ictus dataset Transfer learning

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs control systems Vectors Linear systems Electronic mail

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Synthesis of Dynamic Virtual-Inertia for Microgrid System Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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2025 International Conference on Intelligent control, Computing and Communications, IC3 2025

作者： Justo, Jackson J. Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Makolo, Peter Bansal, Ramesh C. Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia University of Dar es Salaam Department of Electrical Engineering P.O. Box 33335 Dar es Salaam Tanzania United Republic of University of Sharjah Depatment of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331527495

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied. © 2025 IEEE.

关键词： Solar fuels

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Hybrid dimension-based analysis of the energy, exergy, and dynamic response of cross-flow SOFC-CHP system

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Applied Thermal engineering 2025年 274卷

作者： Xia, Lei Madonski, Rafal Sun, Li National Engineering Research Center of Power Generation Control and Safety School of Energy and Environment Southeast University Nanjing210096 China Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland

Some of the solid oxide fuel cell (SOFC) poly-generation systems currently have the problem of high exergy destruction of the air-side heat exchangers, which exceeds that of SOFC. Meanwhile, there are fewer studies on the variable distribution of key components, especially the dynamic distribution characteristics. In this study, a combined heat and power (CHP) system integrating a cross-flow SOFC, an organic Rankine cycle (ORC), and a heating unit is proposed, and distributed parameter modeling is applied to develop a dynamic mechanism model for a two-dimensional cross-flow SOFC and a one-dimensional methane reformer. Then the steady state performance and dynamic response characteristics of the system and key components are comprehensively analyzed. The results show that the total exergy destruction of the air-side heat exchangers is always lower than that of the SOFC under each operating condition. The maximum temperature and current density of SOFC are located at nodes (8,8) and (5,8), respectively. With the increase in fuel utilization (FU), the total system efficiency increases and then decreases, peaking at 83.36 %. Increases in SOFC internal reforming rate and the decrease in inlet gas temperature exacerbate the SOFC temperature distribution non-uniformity. A step change in current causes rapid fluctuations in system performance and in the internal variables of the SOFC. A step change in air flow first changes the cathode oxygen concentration, causing voltage changes. The current density variation trends at each node of the SOFC are different depending on the reactant concentration and temperature. © 2025

关键词： Cross-flow Distributed parameter modeling Dynamic responses Exergy analysis Solid oxide fuel cell

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