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Observer-based finite-time control of conformable Takagi–Sugeno fuzzy systems with time delay

Asian Journal of control 2025年

作者： Kahouli, Omar Gassara, Hamdi Aloui, Ali Elleuch, Zied Bouteraa, Yassine Department of Electronics Engineering Community College University of Ha'il Ha'il Saudi Arabia Laboratory of Science and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Department of Computer Science Community College University of Ha'il Ha'il Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

This paper examines finite-time (F-T) observer-based control of fractional-order nonlinear systems with time delay, employing the Takagi–Sugeno fuzzy (T-SF) approach. This study utilizes the conformable fractional derivative (CFD) as its foundational framework. Sufficient conditions are formulated to guarantee that the augmented vector, consisting of the state estimates and the state estimation errors, stays within a ball centered at the origin. This applies not only to specified initial conditions but also to predefined disturbances. These conditions are then reformulated as linear matrix inequalities (LMIs) conditions, which can be efficiently solved using the LMI toolbox. An illustrative example is given to illustrate the proposed result. © 2025 Chinese automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Delay-sensitive applications

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Data-Driven Reachability Analysis for Piecewise Affine System

arXiv

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arXiv 2025年

作者： Xie, Peng Betz, Johannes Raimondo, Davide M. Alanwar, Amr TUM School of Computation Information and Technology Department of Computer Engineering Technical University of Munich Heilbronn74076 Germany TUM School of Engineering and Design Department of Autonomous Vehicles Systems Technical University of Munich Garching85748 Germany School of Automatic Control Department of Engineering and Architecture University of Trieste Via Valerio 10 Trieste34127 Italy

Hybrid systems play a crucial role in modeling real-world applications where discrete and continuous dynamics interact, including autonomous vehicles, power systems, and traffic networks. Safety verification for these systems requires determining whether system states can enter unsafe regions under given initial conditions and uncertainties—a question directly addressed by reachability analysis. However, hybrid systems present unique difficulties because their state space is divided into multiple regions with distinct dynamic models, causing traditional data-driven methods to produce inadequate over-approximations of reachable sets at region boundaries where dynamics change abruptly. This paper introduces a novel approach using hybrid zonotopes for data-driven reachability analysis of piecewise affine systems. Our method addresses the boundary transition problem by developing computational algorithms that calculate the family of set models guaranteed to contain the true system trajectories. Additionally, we extend and evaluate three methods for set-based estimation that account for input-output data with measurement noise. © 2025, CC BY.

关键词： Convergence of numerical methods

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Frequency stabilization of a sophisticated multi-area interconnected hybrid power system considering non-inertia sources

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International Journal of Electrical Power and Energy Systems 2025年 169卷

作者： Gawad, Diaa M. Magdy, Gaber Ebrahim, M.A. Petlenkov, Eduard Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Cairo Egypt National Energy Control Center Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan81528 Egypt Faculty of Engineering King Salman International University El-Tor46511 Egypt Centre for Intelligent Systems Department of Computer Systems Tallinn University of Technology Tallinn19086 Estonia

This study introduces a novel interconnected power system model, which combines four real areas, integrating conventional power plants with renewable energy sources (RESs). This comprehensive system model aims to address the load frequency control (LFC) challenges that such systems face. Where the integration of RESs into power systems poses significant challenges, particularly in maintaining frequency stability. The LFC approach utilizes a proportional-integral-derivative (PID) controller, meticulously optimized through the Runge-Kutta (RUN) optimizer based on its mathematical principles. Dealing with the uncertainties of RESs and system nonlinearities poses a significant challenge for this controller. The efficacy of the proposed PID controller based on the RUN algorithm is examined by analyzing the frequency stability of the Egyptian power system (EPS), which stays interconnected with neighboring grids in Jordan, Libya, and Sudan. This study accounts for various loading scenarios and system nonlinearities to validate the algorithm's effectiveness. Furthermore, the superior performance of the RUN optimizer is verified by contrasting its results with other widely recognized optimization techniques, such as the Ant Lion optimizer (ALO) and Chernobyl disaster optimizer (CDO). Additionally, the PID controller based on the RUN optimizer is also evaluated against literature that employed a moth swarm algorithm (MSA) to design the PID controller-based LFC in the EPS. Simulation results in the MATLAB environment demonstrate the effectiveness and robustness of the proposed PID controller based on the RUN algorithm. The RUN-based PID controller outperforms other PID controllers optimized using algorithms from the literature (e.g., ALO, CDO, and MSA), achieving superior performance across various contingencies, including load variations, RES uncertainties, and system nonlinearities. The results highlight the effectiveness of the proposed controller compared to those in the l

关键词： Electric power system interconnection

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A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in Machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： computer vision Accuracy Perturbation methods computer network reliability Machine learning Robustness Security Medical diagnosis Resilience Image classification

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Developing a modelling environment of spacecraft solar array in low Earth orbit using real-time telemetry data

Franklin Open

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Franklin Open 2025年 11卷

作者： Mokhtar, Ahmed Ibrahim, Mohamed Hanafy, Mohamed E. ElTohamy, Fawzy H. Amer Elhalwagy, Yehia Z. Aircraft Electric Equipment Department Military Technical College Cairo Egypt Automatic Control Systems Department Military Technical College Cairo Egypt Vice Dean Faculty of Engineering Modern Sciences & Arts University (MSA) Giza Egypt

Spacecraft solar arrays convert sunlight into electrical energy to fulfil the energy requirements of various missions. This work proposes a comprehensive environment for accurate power operation predictions. Specifically, this environment simulates the behaviour of a body-mounted solar array. Furthermore, the designing and modelling processes of the solar array require considering different technical and practical constraints posed by the space environment. These challenges necessitate a thorough evaluation of all potential sources of losses and degradation. Compared to conventional approaches, our novel SSA model incorporates the complete spacecraft mission design scenario, thus it incorporates the operational cyclogram and power budget calculation. To substantiate our proposed method, telemetry data from the commercial LEOS-50 platform is leveraged to develop an experimental, mathematical, and thermal in-orbit model based on GaAs technology. This approach stands out for its exceptional accuracy in predicting the output power characteristics of solar panels. Therefore, it ensures achieving mission requirements from inception to completion in the beginning-of-life and end-of-life stages. The results demonstrate the success of the SSA operation in converting sunlight into electrical energy with a high conversion rate. © 2025 The Authors

关键词： Energy management and control EPS system design Low Earth orbit Orbit illumination Solar array

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Intelligent controller design for the ship steering problem

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IEE PROCEEDINGS-control THEORY AND APPLICATIONS 1996年第1期143卷 17-24页

作者： Enab, YM Faculty of Engineering Automatic Control & Computer Engineering Department El-Mansoura University El-Mansoura Egypt

The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or lesser extent, of dealing with problems of a certain complexity. Fortunately, the human operator (HO) is often expert in keeping the complex control systems on the right track. In this paper a method for controller design has been investigated based on a concept of developing a mathematical model for HO behaviour. The method treats HO behaviour as a dynamic process by itself, transformed from the unknown dynamics of the ship to be controlled. The method of controller design entails observing the human controller, constructing a model of the human controller, and using that model as a basis for an automatic control system. During the observation phase the HO controls the ship and the data representing the state of the ship and human actions are registered. During the modelling phase, a human operator behaviour model (HOBM) is constructed as a neural network and its parameters are estimated. During the testing phase, the HOBM controls the ship and its performance is evaluated under the observation phase conditions and completely different conditions. The method has been successfully applied using computer simulation for the ship steering problem.

关键词： man-machine systems intelligent control neural networks learning systems

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Editorial: Biomedical signals and artificial intelligence towards smart robots control strategies

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Frontiers in Neurorobotics 2025年 19卷 1570870页

作者： Seddik, Hassene Fourati, Hassen Ben Jabeur, Chiraz Khraief, Nahla Department of Computer and Embedded Systems Higher National Engineering School of Tunis Tunis Tunisia Department of Automatic Control Université Grenoble Alpes Auvergne-Rhone-Alpes Saint Martin d'Héres France Ariana Tunisia Tunis Tunisia

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis

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Biomedical Signal Processing and control 2025年 109卷

作者： ZainEldin, Hanaa Bamaqa, Amna Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa A. Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura 35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah 42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu 46421 Saudi Arabia

One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key gaps, including the lack of integration between advanced object detection models like YOLOv9 and YOLOv10 with high-performance classification architectures such as EfficientNet B7, as well as the limited utilization of diverse imaging modalities. This study proposes a novel ensemble model for Breast Cancer Classification and Detection by combining YOLOv9, YOLOv10, and EfficientNet B7 (BC²D-Y/EN) to address these gaps. The proposed model combines the advanced object detection capabilities of YOLOv9 and YOLOv10 with the high-performance classification power of EfficientNet B7. This method aims to improve detection accuracy and computational efficiency in breast cancer identification. The proposed approach is validated using four publicly available datasets: CBIS-DDSM, CBIS-DDSM RGB breast cancer dataset, The Breast1 MRI dataset, and the Breast Ultrasound Tumor Object Detection dataset, all containing expert-annotated cases of benign and malignant tumors. The experimental findings indicate that the suggested model surpasses other models, achieving precision rates of 98.56% with EfficientNet B7, 98.3% with YOLOv9, and 100% with YOLOv9. These findings highlight the advantages of advanced models and diverse imaging methodologies to enhance diagnostic accuracy and effectiveness in breast cancer detection. © 2025 Elsevier Ltd

关键词： Breast cancer detection (BCD) Classification Deep learning (DL) EfficientNet B7 YOLOv10 YOLOv9

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