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Decentralized control of Power System

International Journal of control, Automation and Systems 2025年第5期23卷 1356-1365页

作者： Veselý, Vojtech Ilka, Adrián Ernek, Martin Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkoviċova 3 Bratislava841 04 Slovakia

This paper presents a novel decentralized controller design approach for synchronous generators’ voltage and power control in complex power systems. Unlike traditional methods that require detailed modeling of interconnection dynamics, our method exploits the relationship between the stability of isolated subsystems and that of the overall system by transforming the plant model into a descriptor form with a block-diagonal (decentralized) structure. This transformation enables independent controller synthesis at the subsystem level through a method consisting of three key steps. First, the overall stability of the complex power system is evaluated using a Lyapunov function approach. Second, stability conditions at the subsystem level are derived such that ensuring each subsystem’s closed-loop stability guarantees the stability of the entire system. Third, a decentralized controller is designed using any suitable method (e.g., via extended LMI/BMI conditions), with the guarantee that the closed-loop subsystem stability measure is enhanced relative to the open-loop. The robust controller design is carried out via extended LMI/BMI conditions, ensuring improved dynamic performance and robust performance under bounded disturbances by incorporating variations in subsystem parameters. Simulation results on a multi-generator power system demonstrate improved transient response, robust disturbance rejection, and scalability, making the proposed method an attractive solution for large-scale power system control. © ICROS, KIEE and Springer 2025.

关键词： Decentralized control

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Comparison of Deep Reinforcement Methods and Neuroevolution for Bipedal Walker Learning

Comparison of Deep Reinforcement Methods and Neuroevolution ...

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Cybernetics & Informatics (K&I)

作者： Oliver Halaš Filip Zúbek Ivan Sekaj Faculty of Electrical Engineering and Information Technology Institute of Robotics and Cybernetics Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

This paper presents an in-depth comparison of deep reinforcement learning (DRL) methods and neuroevolutionary techniques for continuous control in robotic movement systems, using the Bipedal Walker environment as a testbed. We evaluate three prominent DRL algorithms: Advantage Actor-Critic (A2C), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO), and NeuroEvolution of Augmenting Topologies (NEAT). The study benchmarks each algorithm under identical computational constraints, focusing on learning progress over a fixed training period and assessing performance on a set of unseen terrain configurations. Results reveal a trade-off between DRL methods’ robustness and NEAT’s structural flexibility, highlighting DRL’s edge in adaptability and reward maximization. This work contributes to a better understanding of each approach’s suitability for complex continuous control problems, offering insights that can guide method selection in robotics and control systems applications.

关键词： Training Legged locomotion Scalability Focusing Robustness Topology Time factors Robots Optimization Faces

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Sim2Real Learning With Domain Randomization for Autonomous Guidewire Navigation in Robotic-Assisted Endovascular Procedures

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IEEE Transactions on Automation Science and engineering 2025年 22卷 13842-13854页

作者： Yao, Tianliang Wang, Haoyu Lu, Bo Ge, Jiajia Pei, Zhiqiang Kowarschik, Markus Sun, Lining Seneviratne, Lakmal Qi, Peng Tongji University College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Department of Control Science and Engineering Shanghai200092 China Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai201620 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Jiangsu Suzhou215131 China Advanced Therapies Siemens Healthineers Ltd. Shanghai201318 China University of Shanghai for Science and Technology Oriental Pan-Vascular Devices Innovation College Shanghai200093 China Bavaria Forchheim91301 Germany Khalifa University KUCARS Department of Mechanical and Nuclear Engineering Abu Dhabi United Arab Emirates Tongji University College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center Shanghai East Hospital Department of Control Science and Engineering School of Medicine Shanghai200092 China

Over the past decade, significant advancements have been made in the research and industrialization of robotic systems for endovascular procedures, yet their clinical application remains relatively limited. Physicians commonly report that these robots lack certain intelligent assistive capabilities during procedures. There has been increasing interest and attempts to apply learning-centered algorithms to the training and enhancement of surgical robot skills. This paper proposes an autonomous navigation algorithm for interventional guidewires that is initially trained solely in a virtual simulation environment and subsequently deployed to a real-world robot. Experimental results demonstrate the feasibility of this approach for real-world applications. The proposed approach can help physicians reduce the learning curve for guidewire manipulation and elevate the robot to a higher level of autonomous operation, thereby breaking through the current bottleneck in the level of intelligence for clinical applications of interventional robots. It also holds promise for bringing intelligent transformation to future interventional procedures. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Ultimate Boundedness and Output Convergence of Prioritized Output Tracking control Under Nonsmooth and Imperfect Feedback Linearization

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IEEE Transactions on Automatic control 2025年

作者： An, Sang-Ik Lee, Dongheui Park, Gyunghoon University of Seoul School of Electrical and Computer Engineering Korea Republic of Tu Wien Autonomous Systems at Institute of Computer Technology Faculty of Electrical Engineering and Information Technology Austria Institute of Robotics and Mechatronics Germany

The concept of priority has been introduced to the robotic systems in 1980 s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input-output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we firstly prioritize the input-output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. Additionally, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method. © 1963-2012 IEEE.

关键词： Feedback linearization

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Real-Time Servo control with Gesture Recognition on Nvidia Jetson Using CNNs

Real-Time Servo Control with Gesture Recognition on Nvidia J...

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Cybernetics & Informatics (K&I)

作者： Ladislav Körösi Slavomír Kajan Michal Kováč Jaromír Skirkanč Ivan Sekaj Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

Artificial intelligence (AI) algorithms are increasingly utilized across various domains, including real-time industrial control. This paper presents a real-time servo system control using hand gestures, detected by a convolutional neural network (CNN) implemented on an NVIDIA Jetson Edge device. The recognized gestures are transmitted via a deterministic industrial protocol (Profinet) to a programmable logic controller (PLC), which drives the servo system through motion function blocks (MFBs). This approach demonstrates significant improvements in real-time control for human-machine interaction (HMI) applications compared to direct CNN implementations on PLCs.

关键词： Accuracy Gesture recognition control systems Cameras Real-time systems Stability analysis Convolutional neural networks Reliability Servomotors Artificial intelligence

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Intersection Model control using Machine Learning Methods

Intersection Model Control using Machine Learning Methods

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Cybernetics & Informatics (K&I)

作者： Ivan Sekaj Jaromír Skirkanić Slavomír Kajan Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

This paper describes the design of a simulation model of intersection control using using neuroevolution. The goal is to optimize the traffic flow by maximizing the throughput of the intersection and minimizing the waiting time of any participant in the traffic situation. Our neuroevolution approach uses a feed-forward neural network which is trained by genetic algorithm. The proposed neuroevolution method is compared with a reinforcement learning method, specifically a Q -learning one. The efficiency of these methods is assessed using suitable simulated training and testing scenarios provided via the model’s environment. These results indicate that the neuroevolutiontrained artificial neural network slightly outperforms the reinforcement learning approach in terms of throughput and waiting times at the intersection. These findings highlight the potential of neuroevolution as a perspective machine learning method.

关键词： Training Q-learning Scalability Artificial neural networks Traffic control Throughput Vehicle dynamics Intelligent transportation systems Testing Genetic algorithms

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Three-factor assessment of rutting factor of modified asphalt binders and mastics in the ageing process

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Mielczarek, Marta Andrzejczak, Karol Slowik, Mieczyslaw Poznan University of Technology Faculty of Civil and Transport Engineering Institute of Civil Engineering Piotrowo 5 Poznań60-965 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Mathematics Piotrowo 3A Poznań60-965 Poland

This paper discusses the research and multivariate analyses of the rheological properties of asphalt binders and mastics containing various mineral fillers and SBS copolymer-modified bitumen. In the experimental design employed, three types of mineral filler were used: limestone, basalt, or granodiorite. To determine the effect of ageing processes on the rheological properties of the obtained mastics, all asphalt binders tested were subjected to short-term (technological) ageing simulated under laboratory conditions using the RTFOT method. The main aim of this study was to evaluate the rutting resistance of modified binders and asphalt mastics depending on such factors as: the type of mineral filler, the percentage of SBS copolymer, temperature, and the influence of the ageing process. A three-way ANOVA was used to test for rutting resistance. The results of laboratory tests and statistical analyses made it possible to assess the significance of the factors examined in the research on the rutting resistance of asphalt binders and mastics, both before and after short-term ageing. © 2025 The Author(s).

关键词： Analysis of variance (ANOVA)

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