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Event-Triggered Extended Dissipative FTB for T-S Fuzzy Switched Systems With Mismatched Phenomena and Deception Attacks: A Multidomain Framework

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IEEE Transactions on Cybernetics 2024年第9期54卷 4928-4938页

作者： Chen, Haiyang Zong, Guangdeng Su, Shun-Feng Gao, Fangzheng Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Jiangsu Collaborative Innovation Center for Smart Distribution Network Nanjing211167 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Nanjing Institute of Technology School of Automation Nanjing211167 China

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results. © 2013 IEEE.

关键词： controllers

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loT-enabled Rapid Global Maximum Power Point Tracking for Multiple Photovoltaic Inverters 15

loT-enabled Rapid Global Maximum Power Point Tracking for Mu...

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15th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2024

作者： Zhou, Kangjia Gao, Feng Qin, Caiyun Meng, Xiangjian School of Electrical Engineering Shandong University Jinan China School of Control Science and Engineering Shandong University Jinan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350361001

Maximum power point tracking (MPPT) is a crucial technology for enhancing photovoltaic (PV) array power generation efficiency. Under scenarios with partial shading, the output power characteristic curve of a PV array often displays multiple peaks, necessitating the use of global peak-seeking techniques. Particle Swarm Optimization (PSO) and its advanced variants have been widely adopted to address global optimization challenges. However, the current landscape primarily includes PSO based MPPT methods designed for single inverters, which tend to exhibit slow optimization speeds. This paper presents an Internet of Things (IoT)-based multi- inverters approach for global MPPT. In this approach, all inverters within the network are treated as a single particle. This transformation shifts the conventional serial PSO operation into a parallel one, enabling data exchange among inverters to jointly track the maximum power point. Simulation results demonstrate that the proposed methodology significantly accelerates the optimization process, effectively mitigating the limitations of traditional approaches. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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Optimal Cooperative Secondary control for Islanded DC Microgrids via a Fully Actuated Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 405-417页

作者： Yi Yu Guo-Ping Liu Yi Huang Peng Shi IEEE Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Electrical and Mechanical Engineering the University of AdelaideSA 5005Australia Research and Innovation Centre Obuda University1034 BudapestHungary

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

关键词： DC microgrids distributed control high-order fully actuated system approach linear quadratic regulator microgrid modeling secondary control

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Managing Battery Performance Degradation Using Physics-Informed Learning Scheme for Multiple Health Indicators

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7309-7321页

作者： Qin, Linxiao Sun, Tao Sun, Xi-Ming Xia, Weiguo Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Tsinghua University Department of Automation Beijing100084 China

To address the challenges in battery health management, this article introduces a physics-informed neural network predictor-estimator scheme. In the framework, the predictor forecasts health degradation, setting benchmarks for the estimator, while the estimator provides calibrations for the predictor as a substitution for infeasible measurement techniques. For the predictor design, a linear-exponential model is employed for two-stage degradation prediction, enabling online parameter updates and knee point detection. For the estimator design, three neural networks embedded with Coulomb counting and equivalent circuits are proposed to estimate the state of charge (SOC), internal resistance, and capacity, respectively. Experimental results on the A123 dataset demonstrate the superior accuracy of our scheme, with a maximum R2 improvement of 22.8% in prediction tasks and 11.5% in estimation tasks, while maintaining lightweight network structures with fewer than 1000 floating-point operations (FLOPs) and 200 parameters. © 2015 IEEE.

关键词： Electronic health record

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Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

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Journal of Energy Chemistry 2024年第5期92卷 738-758页

作者： Chuanxin Fan Kailong Liu Yaxing Ren Qiao Peng School of Automation Nanjing Institute of TechnologyNanjing 211167JiangsuChina School of Control Science and Engineering Shandong UniversityJinan 250100ShandongChina School of Engineering University of LincolnLincoln LN67TSUK Information Technology Analytics&Operations GroupQueen’s University BelfastBelfast BT95EEUK

Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of electric vehicles and energy storage systems in the *** design of battery state estimation and control algorithms in battery management systems is usually based on battery models,which interpret crucial battery dynamics through the utilization of mathematical ***,the investigation of battery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery *** methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this *** review has undertaken an analysis and discussion of characterization methods,with a particular focus on the motivation of battery system ***,this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical *** aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries,with the intention of promoting the advancement of efficient battery technology for real-world applications.

关键词： Lithium-ion battery Battery dynamics Nonlinear characterization Nonlinear battery model

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Event-triggered Adaptive Penalty Model Predictive control for Hybrid Energy Storage System 3

Event-triggered Adaptive Penalty Model Predictive Control fo...

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3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Wang, Siqi Li, Zihao Guo, Yuchao Fan, Yuan Anhui University School of Electrical Engineering and Automation Anhui Hefei China Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou China

ISBN: (纸本)9798350352658

This work proposes an optimized voltage stabilization approach for electric vehicles (EVs) integrated with PV systems and a hybrid energy storage system (HESS). Specifically, the combination of Adaptive Penalty Model Predictive control (APMPC) and an Event-triggered (ET) mechanism is utilized to effectively regulate the PV output and storage response when the solar irradiance increases suddenly. This approach dynamically adjusts the operation of system to maintain a stable DC bus voltage. Simulations validate the effectiveness of the proposed method in stabilizing the voltage and improving energy utilization, thereby contributing to the development of sustainable and efficient EVs systems. © 2024 IEEE.

关键词： Predictive control systems

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Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

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Journal of Energy Chemistry 2024年第9期96卷 679-697页

作者： Xinrong Huang Jinhao Meng Wei Jiang Wenjie Liu Kailong Liu Yipu Zhang Daniel-Ioan Stroe Remus Teodorescu School of Energy and Electrical Engineering Chang’an UniversityXi’an 710018ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Highway Chang’an UniversityXi’an 710018ShaanxiChina School of Automation Northwestern Polytechnical UniversityXi’an 710072ShaanxiChina School of Control Science and Engineering Shandong UniversityJinan 250100ShandongChina Department of Energy Aalborg UniversityAalborg 9220Denmark

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold *** current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating *** paper introduces the recent advances in AC heating from the perspective of practical EV ***,the performance degradation of EVs in low-temperature environments is introduced *** concept of AC heating and its research methods are ***,the effects of various AC heating methods on battery heating performance are *** on existing studies,the main factors that affect AC heating performance are ***,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and *** evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted ***,the perspectives and challenges of AC heating are *** paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

关键词： Lithium-ion battery Low temperature Alternating current heating Heater Electric vehicle

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Dual Sparse Variational Autoencoder-driven Interactive Estimation of Distribution Algorithm with User Generated Contents

Dual Sparse Variational Autoencoder-driven Interactive Estim...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Yang, Hang Bao, Lin Sun, XiaoYan Peng, YuMeng Jiangsu University of Science and Technology School of Automation Zhenjiang China China University of Mining and Technology School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798331517021

Personalized search and recommendation tasks in a big data environment have attracted wide attention from researchers while also presenting significant *** paper proposed a dual sparse variational autoencoder-driven interactive estimation of distribution algorithm with user generated contents, which considers digging deeper into user generated contents and combining with intelligent evolutionary optimization methods. Firstly, user generated contents in web are extensively collected to build a user interest preference model based on dual sparse variational autoencoders to extract users' preference features. Secondly, the distribution probability is reconstructed according to the neurons of the output layer of the sparse variational autoencoders in the user interest preference model. Combining with Gaussian probability density function, a estimation of distribution sampling probability model based on the user interest preferences is designed in the framework of an interactive estimation of distribution algorithm to generate new individuals with the user preference information. Meanwhile, a user evaluation surrogate model based on the user interest preferences is established by integrating matrix factorizations to participate in a personalized evolutionary search process. Finally, according to new user generated contents and model management mechanisms, the user interest preference model and related models are dynamically updated to guide the interactive personalized evolutionary search for personalized recommendation task. Extensive experiments on various actual public datasets are executed to validate the feasibility, effectiveness and interpretability of the proposed algorithm. It improves the performance of personalized search and recommendation methods. © 2024 IEEE.

关键词： Matrix factorization

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A Novel Three-DOF Transverse Flux Spherical Actuator With Decoupling Mechanism 26

A Novel Three-DOF Transverse Flux Spherical Actuator With De...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Liu, Xiaoshuai Yan, Liang Xiang, Pengjie He, Xinghua Du, Nannan Wu, Chong Beihang University School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing China

ISBN: (纸本)9798350317589

This paper proposes a novel multi-DOF spherical actuator based on transverse flux and three-DOF decoupling mechanism. Firstly, the structure and working principle of the new multi-DOF spherical actuator are presented. Then, numerical simulations are performed to analyze its magnetic field and torque, resulting in the basic performance curve of the spherical motor. Finally, The structure of this spherical actuator is optimized by numerical simulation. The optimization results show that the performance of the optimized spherical actuator has been effectively improved in terms of torque ripple, output torque and control precision. © 2023 IEEE.

关键词： Spheres

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