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Aggregation and Robustness of Reversible Switched Linear Systems

Journal of Systems Science & Complexity 2025年第2期38卷 821-836页

作者： LI Xuehui ZHENG Zhou DONG Hairong SUN Zhendong College of Electrical Engineering and Automation Shandong University of Science and Technology

In this paper, the authors propose an approach to properly aggregate a reversible discretetime switched linear system and prove that, for any n-dimensional exponentially stabilizable switched system, the authors could design up to n linear gain matrices, such that the extended system is also exponentially stabilizable as a switched autonomous system. By utilizing the pathwise state feedback switching strategy of the switched autonomous system, the original system is aggregated into a piecewise linear system that is step-wise norm contractive and exponentially stable. The authors also develop a robust switching design mechanism that simultaneously achieves exponential stability, structural stability, and input-to-state stability for the closed-loop system. A numerical example is presented to demonstrate the effectiveness of the proposed design scheme.

关键词： Aggregation robustness stabilization switched linear systems

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An overview of recent advances in event-triggered control

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Science China(Information Sciences) 2025年第6期68卷 222-252页

作者： Xian-Ming ZHANG Qing-Long HAN Xiaohua GE Derui DING Boda NING Bao-Lin ZHANG School of Engineering Swinburne University of Technology Department of Electrical and Electronic Engineering Auckland University of Technology College of Automation and Electronic Engineering Qingdao University of Science & Technology

Event-triggered control(ETC) offers an efficient strategy for significantly reducing communication and computation resources in networked systems by triggering control updates only when necessary. This study provides an overview of recent advances in ETC. First, data-driven(or model-free) ETC, which has gained significant attention in recent years, is reviewed for linear systems with and without unknown disturbances. Second, co-design issues are deeply analyzed for both state feedback and dynamic output feedback control. Third, the separation principle is thoroughly examined in the context of event-triggered observer-based output feedback control. Fourth, some insightful discussions are made on the ideal execution property of event-triggered schemes, as well as the modeling of ETC under packet dropouts. Finally, several challenging issues for future research are outlined.

关键词： event-triggered control data-driven control observer-based output feedback control separation principle Zeno behavior

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Distributed Leaderless Optimal Output Consensus of Heterogeneous Multi-Agent Systems

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Journal of Systems Science & Complexity 2025年第2期38卷 671-690页

作者： ZHANG Liping ZHANG Huanshui XIE Lihua College of Electrical Engineering and Automation Shandong University of Science and Technology School of Control Science and Engineering Shandong University School of Electrical and Electronic Engineering Nanyang Technological University

The distributed optimal output synchronization problem for the leaderless heterogeneous multi-agent system with a general global cost function is investigated for the first time by linear quadratic(LQ) optimal control theory. Conventional algorithms for heterogeneous systems are quite complex, requiring the design of a virtual reference generator and the solving of regulation *** paper presents a novel distributed asymptotically optimal controller by incorporating the design of distributed observer and feedforward controller. A general form of the distributed controller is obtained by solving an augmented algebraic Riccati equation, which is parallel to classical optimal control theory. The optimal topology is an arbitrary directed graph containing only a spanning tree. It is shown that the proposed algorithms outperform the traditional consensus methods in the convergence speed by selecting proper observer gain matrices, and eliminate the reliance on the nonzero eigenvalues of Laplacian matrix. Simulation example further demonstrates the effectiveness of the proposed scheme and a faster superlinear convergence speed than the existing algorithm.

关键词： Distributed feedforward control heterogeneous systems leaderless output consensus optimal control

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Solar PV-fed cascaded multilevel inverters employing zeta converter

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International Journal of Power and Energy Conversion 2025年第1期16卷 13-37页

作者： Naqvi, Aquib Mehdi Tripathi, Pushkar Singh, S.P. Electrical Engineering Department Institute of Engineering and Technology Lucknow India Electrical Engineering Department Rajkiye Engineering College Ambedkar Nagar India

The photovoltaic cell produces DC power, while residential, industrial loads and most modern equipment typically require AC power. Thus, there is a need for power converters that can efficiently convert DC to AC. Although two-level inverters are commonly used for this purpose, they suffer from higher total harmonic distortion (THD) and switching stresses. Multilevel inverter (MLI) technology emerges as an effective solution for high-power medium-voltage energy control. This paper examines cascaded multilevel inverters (CMLIs) of varying levels and multiple-carrier sinusoidal switching techniques, conducting a comparative analysis to identify the most suitable switching technique for implementation in solar stand-alone MLIs. Additionally, the study adjusts the duty ratio of the Zeta converter to optimise power extraction from the solar PV system. The primary focus is on the application of CMLIs in stand-alone solar photovoltaic systems, with the study conducted in the Simulink/MATLAB environment. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Harmonic distortion

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Recent Advances in Sn-Based Heterojunction-Type Anode Materials for Alkali-Ion Batteries

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Carbon Energy 2025年第5期7卷 140-175页

作者： Hui Li Zhiqiang Liu Lei Li Yehong Zhang Zeheng Li Huixin Lan Zhenhe Zhu Yuchen Wu Jiajia Li Chuanbo Zheng Jun Lu School of Metallurgy Engineering Jiangsu University of Science and Technology Suzhou Institute of Technology Jiangsu University of Science and Technology Department of Mechanical and Electrical Engineering Hebei Vocational University of Technology and Engineering College of Chemical and Biological Engineering Zhejiang University

The urgent demand for clean energy solutions has intensified the search for advanced storage materials, with rechargeable alkali-ion batteries(AIBs) playing a pivotal role in electrochemical energy storage. Enhancing electrode performance is critical to addressing the increasing need for high-energy and high-power AIBs. Next-generation anode materials face significant challenges, including limited energy storage capacities and complex reaction mechanisms that complicate structural ***-based materials have emerged as promising candidates for AIBs due to their inherent advantages. Recent research has increasingly focused on the development of heterojunctions as a strategy to enhance the performance of Sn-based anode materials. Despite significant advances in this field, comprehensive reviews summarizing the latest developments are still sparse. This review provides a detailed overview of recent progress in Sn-based heterojunction-type anode materials. It begins with an explanation of the concept of heterojunctions, including their fabrication, characterization, and classification. Cuttingedge research on Sn-based heterojunction-type anodes for AIBs is highlighted. Finally, the review summarizes the latest advancements in heterojunction technology and discusses future directions for research and development in this area.

关键词： characterization methods electrochemical performance heterojunction Sn-based anode materials synthesis methods

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Extension of Distribution Transformer Life in the Presence of Smart Inverter-based Distributed Solar Photovoltaic Systems

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 88-95页

作者： Kanhaiya Kumar Saran Satsangi Ganesh Balu Kumbhar Department of Electrical and Electronics Engineering Motihari College of EngineeringMotihariBiharIndia Department of Electrical Engineering National Institute of Technology(NIT)PatnaPatnaBiharIndia Department of Electrical Engineering Indian Institute of Technology(IIT)RoorkeeRoorkeeUttarakhandIndia

A transformer is an essential but expensive power delivery equipment for a distribution *** many distribution utilities worldwide,a sizable percentage of transformers are near the end of their designed *** the same time,distribution utilities are adopting smart inverter-based distributed solar photovoltaic(SPV)systems to maximize renewable *** central objective of this paper is to propose a methodology to quantify the effect of smart inverter-based distributed SPV systems on the aging of distribution *** proposed method is first tested on a modified IEEE-123 node distribution *** that,the procedure is applied to a practical distribution system,i.e.,the Indian Institute of technology(IIT)Roorkee campus,*** transformer aging models,alongside advanced control functionalities of grid-tied smart inverter-based SPV systems,are implemented in *** open-source simulation tool(OpenDSS)is used to model distribution *** analyze effectiveness of various inverter functionalities,time-series simulations are performed using exponential load models,considering daily load curves from multiple seasons,load types,current harmonics,*** show replacing a traditional inverter with a smart inverter-based SPV system can enable local reactive power generation and may extend the life of a distribution *** results demonstrate,simply by incorporating smart inverter-based SPV systems,transformer aging is reduced by 15%to 22%in comparison to SPV systems operating with traditional inverters.

关键词： Distribution transformer loss of life smart inverter functions solar photovoltaic systems thermal models Volt-VAr control

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Examining AI-Powered ADHD Diagnosis: Current Trends, Key Challenges, and Future Directions in the Field

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IEEE Access 2025年 13卷 93148-93177页

作者： Ul Ain, Qurat Jawed, Soyiba Rauf Subhani, Ahmad Haider Butt, Wasi Usman Akram, Muhammad National University of Sciences and Technology College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad44000 Pakistan National University of Sciences and Technology College of Electrical and Mechanical Engineering Department of Electrical Engineering Islamabad44000 Pakistan

Attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disease affecting the mental health of children and adults. Individuals with ADHD show various symptoms such as inattention, hyperactivity, and impulsivity. Early diagnosis of ADHD helps to alter neural connections and refine symptoms. The clinical practice to diagnose ADHD is through subjective measures and does not significantly capture the underlying structural and functional mechanisms of the brain. Therefore, it is crucial to explore other approaches such as Artificial Intelligence (AI) to improve the accuracy and efficacy of ADHD diagnosis. Consequently, in this article we systematically investigate various Machine Learning (ML) and Deep Learning (DL) approaches as well as different diagnostic tools or modalities employed for the identification of ADHD. Particularly, a Systematic Literature Review (SLR) is conducted to review and analyze 98 selected studies published from 2021 to 2024. Subsequently, the selected studies are grouped into five categories based on the modalities utilized in these studies: physiological signals (37), magnetic resonance imaging (31), questionnaires (11), motion data (8), and others (11). We also analyze AI models which indicates that 45 studies utilized ML models, 33 studies employed DL models, and 20 studies used both. However, there are still some gaps in current research such as a lack of publicly available datasets except MRI and EEG. Although datasets for MEG and actigraphy exist, but they are underexplored and have been utilized in only a few studies. While DL models like CNNs and ANNs have been increasingly applied in recent years for ADHD diagnosis, there is a shortage of advanced DL models, including transfer learning approaches like ResNet and VGG. Additionally, there is a lack of interpretability in AI models, particularly DL models. Furthermore, most studies focus on individual modalities for ADHD diagnosis, and despite many studies showing

关键词： Electrocardiography

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Intelligent identification method of insulation pull rod defects based on intactness-aware Mosaic data augmentation and fusion of YOLOv5s

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High Voltage 2024年第5期9卷 1171-1182页

作者： Changyun Li Yuze Hua Yilin Liu Kai Liu Sanyi Zhang College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdaoChina College of Electrical Engineering Southwest Jiaotong UniversityChengduChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina

The authors introduce the intactness-aware Mosaic data augmentation strategy,designed to tackle challenges such as low accuracy in detecting defects in insulation pull rods,limited timeliness in intelligent analysis,and the absence of a comprehensive database for information on insulation pull rod *** proposed strategy incorporates the YOLOv5s algorithm for detecting defects in insulation pull ***,the YOLOv5s network was constructed,and a dataset containing photos of insulation pull rods with white spots,fractures,impurities,and bubble flaws was compiled to capture images of *** research presented a data enhancement approach to improve the images and establish a dataset for insulation pull rod *** YOLOv5s algorithm was applied for both training and testing purposes.A comparative analysis was conducted to assess the detection performance of YOLOv5s against a conventional target detector for identifying defects in insulation pull ***,the utility of Mosaic's data augmentation technique,which incorporates intactness awareness,was evaluated to enhance the accuracy of identifying insulation pull rod *** research findings indicate that the YOLOv5s algorithm is employed for intelligent detection and precise localisation of *** intactnessaware Mosaic data augmentation strategy significantly improves the accuracy of detecting faults in insulation pull *** YOLOv5s model used achieves a performance index mAP@0.5:0.95 of 0.563 on the test set,distinct from the training set *** a threshold of 0.5,the mAP@0.5 score is 0.904,indicating a substantial improvement in both detection efficiency and accuracy compared to conventional target detection *** approaches for identifying defects in insulation pull rods are introduced.

关键词： Mosaic insulation defects

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A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries

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Journal of Energy Chemistry 2025年第6期105卷 261-279页

作者： Chunsong Lin Longxing Wu Xianguo Tuo Chunhui Liu Wei Zhang Zebo Huang Guiyu Zhang School of Mechanical Engineering Sichuan University of Science and Engineering School of Information Engineering Southwest University of Science and Technology College of Intelligent Manufacturing Anhui Science and Technology University School of Mechanical and Electrical Engineering Guilin University of Electronic Technology

Accurately estimating the battery state of health（SOH） is essential for ensuring the safe and reliable operation of battery systems of electric ***,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains *** address these problems,this paper develops a lightweight two-stage physicsinformed neural network（TSPINN） method for SOH estimation of lithium-ion batteries with different ***,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model（ECM）.Additionally,incremental capacity（IC） feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH ***,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss *** effectiveness of the TSPINN method was confirmed through the experimental data for LiNi0.86Co0.11Al0.03O2（NCA） and LiNi0.83Co0.11Mn0.07O2（NCM） battery materials under different temperature *** final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error（MAE） of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,*** results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.

关键词： Lithium-ion battery Physics-information neural network State of health Voltage relaxation

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