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International Conference on Electrical Machines, ICEM

作者： Luka Pravica Filip Jukić Damir Sumina Igor Erceg Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

Synchronous reluctance machines (SynRMs) are increasingly used in various applications, such as energy conversion systems and electric drives. In addition to standard control algorithms, some of these applications also require the implementation of flying start methods that aim to estimate the rotor speed and angle before the SynRM is connected to the converter. In this way, synchronization between the SynRM and the converter is achieved, and the inrush current is reduced. This paper proposes a new method for the flying start of SynRMs based on symmetric and opposite active voltage vectors and a second-order generalized integrator (SOGI) with a frequency-locked loop (FLL). Applying such a voltage vector pattern to the terminals of a rotating SynRM induces phase current spikes whose sampled counterparts have a sinusoidal waveform. According to the derived mathematical model of the SynRM in the stationary $\alpha\beta$ reference frame, the induced $\beta$ -axis current contains no DC offset and is therefore passed to the SOGI-FLL structure to estimate the rotor angle and speed. Moreover, applying the opposite active vectors ensures a lower current distortion, guarantees the accuracy of the estimation and reduces the generated torque. The proposed method was verified by simulations in Matlab Simulink.

关键词： Frequency locked loops Torque Software packages Rotors Estimation Reluctance machines Vectors Mathematical models Torque measurement Matlab

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Cycle-by-Cycle Optimized Active Gate Drive for SiC MOSFETs

Cycle-by-Cycle Optimized Active Gate Drive for SiC MOSFETs

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International (EPE-PEMC) Power Electronics and Motion Control Conference

作者： Tomislav Ivaniš Marinko Kovačić Martin Makar Željko Jakopović Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350385236

ISBN: (纸本)9798350385243

Recently, active gate drivers (AGD) have been proposed as a solution to regulate fast switching for Silicon carbide (SiC) MOSFETs. This paper explores the optimization and optimum control of AGD for SiC MOSFETs to improve efficiency and potentially reduce electromagnetic interference (EMI) through adaptive switching strategies. It presents a comparative analysis of two AGD methods, pullback-like and staircase-like sequences, highlighting their impact on switching dynamics and EMI mitigation. By utilizing simulation models, the study evaluates the feasibility of pre-mapping and multi-objective optimization for real-time control and investigates the theoretical spectral amplitude calculation to achieve targeted EMI attenuation. The research outlines the use of two switching optimization approaches, and applies them to a three-phase inverter scenario, aiming to demonstrate the advantages of optimized AGD over conventional methods. This study in general focuses on advanced gate-driving techniques for power electronics giving the guidelines for future research in gate-driving strategies.

关键词： MOSFET Silicon carbide Shape Electromagnetic interference Switches Logic gates Inverters Real-time systems Voltage control Optimization

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Revised Correction Factors in the Specht's Model for Calculation of the Transformer Inrush Current

Revised Correction Factors in the Specht's Model for Calcula...

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International Conference on Electrical Machines, ICEM

作者： Damir Žarko Zlatko Hanić Department of Electrical Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

In this study, correction factors are introduced for Specht's analytical model, which is used to estimate the inrush current peaks of transformers and their decay rates. The factors are derived for a set of five transformers using a least squares approach to minimize the discrepancies between the inrush current values from the Specht's model and the transient 2D finite element (FE) simulations. The 2D FE model uses an equivalent permeability of air and copper to mimic the flux linkage vs. current characteristics of the 3D FE model and effectively accounts for the effects of air core inductance at extreme saturation. The study considers the winding connections YNyn and YNd under the assumption that the connection to the grid is made on both the high voltage and the low voltage side.

关键词： Analytical models Three-dimensional displays Atmospheric modeling Windings Transformer cores Finite element analysis Power transformers Circuit faults Transient analysis Inrush current

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Comparison of Voltage Balancing Methods for Supercapacitor-Based Elevator Energy Storage

Comparison of Voltage Balancing Methods for Supercapacitor-B...

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International (EPE-PEMC) Power Electronics and Motion Control Conference

作者： Martin Makar Martina Kutija Marinko Kovačić Tomislav Ivaniš Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350385236

ISBN: (纸本)9798350385243

This paper specifies the requirements for voltage balancing methods for supercapacitor-based Elevator Energy Storage Systems (EESS) and analyses the suitability of existing voltage balancing methods for use in EESS. Application-specific requirements for supercapacitors in EESS are given, focusing on the balancing current and the balancing speed. The existing five groups of active balancing methods: adjacent cell to cell, direct cell to cell, cell to bank, bank to cell and any cell to any cell are analysed and their suitability for application in EESS based on supercapacitors is verified by simulation tests. The simulation tests are performed for a supercapacitor bank consisting of 100 cells. The methods are tested for two cases: when the balancing takes place after charging the supercapacitor bank and when the balancing takes place during the charging process of the SC bank. The advantages and disadvantages of each method are discussed as well as their suitability for supercapacitor-based EESS.

关键词： Low voltage Simulation Focusing Voltage Supercapacitors Capacitance Elevators Power electronics Voltage control Motion control

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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Multivariable Decoupling Control of Civil Turbofan Engines Based on Fully Actuated System Approach

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Journal of Systems Science & Complexity 2023年第3期36卷 947-959页

作者： PENG Kai WANG Hongxia ZHANG Huanshui ZHANG Zhaorong YANG Fan School of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China School of Electrical and Automation Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Applied Mathematics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina. Xi’an Aeronautics Computing Technique Research Institute AVICXi’an 710068China

Gas turbine engines must be operated by means of control,and how to achieve multivariable control decoupling with aero-engine control constraints is an open thorny issue attracting increasingly more *** paper considers the multivariable decoupling problems of aero-engines by using a compound controller,which originates from the fact that it is impossible to eliminate all the nonlinear dynamics of system to obtain desired constant linear closed-loop system by using full actuated control because of modeling errors and some physical *** controllers are involved in the compound *** is a fully actuated controller and the other is classical feedback *** order to use fully actuated control and maintain the accuracy of engine model,a full state scheduling linear parameter-varying(LPV)modeling method is proposed based on fuzzy neural network *** a general input matrix of the system,its generalized inverse is applied to design fully actuated controller to result in a pseudolinear *** with a feedback controller and control limiter,the control synthesis is *** simulation shows that the proposed method is possessed of a better decoupling and tracking effect compared with traditional control approach.

关键词： Compound control decoupling control fully actuated control turbofan engine

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Emo-LSTM: An Emotion Recognition Model of ECG Data Based on Long Short-Term Memory 24th

Emo-LSTM: An Emotion Recognition Model of ECG Data Based on ...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Tian, Zhen Liu, Haoting Mi, De Yi, Dewei Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China College of Computing Birmingham City University BirminghamB4 7BD United Kingdom Department of Computing Science University of Aberdeen AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789819771387

In order to accurately monitor human emotions during work, we develop a deep learning model based on electrocardiogram (ECG) data. First, we set up an ECG data acquisition system and generate standard data set. Second, a model combining convolutional neural network (CNN), Long Short-Term Memory (LSTM) and attention mechanism is used for training and testing. Finally, the recognition results are evaluated using evaluation metrics such as Precision, Recall and F1 Score. Experimental results show that the Precision of the model to identify abnormal emotional states is 85.48%, the Recall is 92.16%, and the F1 score is 88.68%. The development of this technology provides a new tool for emotional monitoring in workplace, which promises to improve productivity and safety through timely interventions. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Convolutional neural networks

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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