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CPSS Transactions on Power Electronics and Applications 2025年第1期10卷 110-119页

作者： Kaining Fu Jiangtao Tu Wei Chen Linsen Huang Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Electrical Engineering and Automation Fuzhou University Fuzhou China

Isolated power converters are widely used for its safety and flexible adjustment between input and output voltage range. EMI occurs due to the presence of switching process. The existence of parasitic parameters in transformers causes the work of EMI prediction in isolated DC-DC power converters more complicated. Parasitic parameters in transformer are the crucial propagation paths for CM noise conduction. For improving the accuracy of EMI prediction, this paper developed a wide-band frequency transformer model based on the two-capacitance model, further considering both the effect capacitive and inductive coupling on conductive common mode noise. Furthermore, the influence of permeability versus frequency of Mn-Zn ferrite on CM transmission, is investigated in detail. Two-port measurement is used for the validation of the proposed high frequency model. The experiment results demonstrate that the proposed wind-band frequency transformer model can well predict the CM noise behavior in the frequency range of 100 kHz to 100 MHz.

关键词： Windings Transformers Capacitance Noise Electromagnetic interference Couplings Solid modeling Predictive models Power transformer insulation Magnetic flux

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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A Novel Integrated Energy Management Strategy of Energy Storage System for a Pure Electric-Driven Mining Hydraulic Excavator

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Chinese Journal of Mechanical engineering (English Edition) 2025年第1期38卷 1-17页

作者： Qin, Tao Li, Yunhua Quang, Long Yao, Yu Yang, Liman School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing101399 China Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Tai Yuan030024 China

Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs. However, pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power, leading to significant fluctuations in the power demand of the grid, which in turn affects production. To solve the above problem, a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object. Moreover, supercapacitors and DC/DC converter, as the energy storage system (ESS) adjust the output power of the grid and recover the braking kinetic energy of the swing platform. Subsequently, a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably. Specifically, the working modes of the ESS are determined by the DC bus voltage and state of charge (SOC) of the supercapacitor. Next, the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules. In addition, a digital prototype of the excavator was verified using an original machine test. The performance of the different strategies and driven systems were analyzed using digital prototypes. The results showed that, compared with traditional excavators with diesel engines, the operational cost of the developed excavators was reduced by 54.02%. Compared to pure electric-driven excavators without an ESS, the peak power of the grid for the developed excavators was reduced by 10%. This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS. © The Auth

关键词： Predictive control systems

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Balancing Precision and Speed: Introducing The Performance Efficiency Evaluation Ratio (PEER) in Visual Odometry

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IEEE Access 2025年 13卷 89874-89904页

作者： Atilgan, Cem Mercimek, Muharrem Kırklareli University Department of Mechatronics Engineering Kırklareli Turkey Yildiz Technical University Department of Control and Automation Engineering Istanbul Turkey

Visual odometry is extensively utilized in robotics, autonomous navigation, and augmented reality to estimate motion by analyzing sequential images. Provides high precision without relying on external sensors such as GPS. However, real-time visual odometry systems must strike a balance between computational correctness and speed, a critical yet often overlooked trade-off in existing evaluation methods. Traditional metrics such as ATE, RPE, RMSE, FPS, and RTF typically assess precision or efficiency in isolation and lack adaptability to real-time constraints. To address this limitation, we propose the Performance Efficiency Evaluation Ratio (PEER), a novel, adaptive, and lightweight metric that jointly evaluates algorithm performance based on both fidelity and computation time. PEER incorporates a tunable weighting parameter to prioritize performance, speed, or a balanced trade-off, and employs normalization techniques to ensure comparability across different algorithms and systems. We evaluated PEER using the KITTI dataset with various feature extraction algorithms (SIFT, ORB, BRISK, KAZE, AKAZE) and matching algorithms (BF, FLANN). The findings indicate that PEER not only distinguishes performance effectively across various scenarios but also shows strong alignment with established MCDM methods such as TOPSIS and VIKOR, all while maintaining lower computational complexity and greater suitability for real-time deployment. Overall, PEER provides a robust and flexible framework for optimizing visual odometry systems. © 2013 IEEE.

关键词： Augmented reality

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A Seamless Pedestrian Localization System Based on GNSS/IμUWB/MAP Integration

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IEEE Internet of Things Journal 2025年第11期12卷 16653-16667页

作者： Zhang, Shuning Tang, Hongjing Chen, Liangming Gao, Yufeng Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Pedestrian localization has broad applications. However, achieving seamless positioning in complex environments remains a significant challenge due to factors like environmental occlusion and indoor-outdoor transitions. This paper proposes a combined localization framework using Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and MAP data to address this issue. In the framework, a three-step GNSS localization method is proposed to enhance accuracy and stability by eliminating Non-Line-of-Sight (NLOS) effects and filtering anomalies. Inertial Navigation System (INS) and Pedestrian Dead Reckoning (PDR) models are integrated to improve local precision and stability. UWB, known for its superior localization capabilities, has been integrated into some smartphones and is promising for large-scale indoor applications. The paper presents a gradient descent-based optimal anchor deployment algorithm and localization methods for varying anchor quantities. Additionally, a non-stationary Markov model fuses map data for trajectory prediction and correction. Finally, anomaly detection filtering and low-pass filtering are applied to the corrected trajectories, yielding high accuracy and stability. Experiments on the Honor V20 and iQOO U3 smartphones showed significant improvements compared to the baseline algorithm. Outdoor Root Mean Square Error (RMSE) was reduced by 44.4% (10.83m) for the Honor V20 and 40.0% (10.77m) for the iQOO U3. Additionally, outdoor Standard Deviation (STD) improved by 43.9% (4.80m) and 43.5% (5.50m), respectively. In transitional areas, RMSE improved by 4.61% (10.77m) and 19.6% (4.76m), respectively. Indoor RMSE also showed improvements, with the Honor V20 reducing it by 8.00% (3.79m) and the iQOO U3 by 5.78% (3.91m). © 2025 IEEE.

关键词： Location awareness Global navigation satellite system Accuracy Pedestrians Filtering Sensors Prediction algorithms Predictive models Smart phones Kalman filters

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Fault Detection Unknown Input Observer for Local Nonlinear Fuzzy Autonomous Ground Vehicles System Based on a Joint Peak-to-Peak Analysis and Zonotopic Analysis Threshold

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7226-7236页

作者： Li, Yi Dong, Jiuxiang Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Liaoning Shenyang China

This paper focuses on the fault detection problem of local nonlinear fuzzy autonomous ground vehicles systems with disturbances by developing set membership estimation. The intractable influence of disturbances and local nonlinear part on the design of fault detection thresholds is the principal difficulty in fault detection using the existing set membership estimation methods. To this end, a fault detection unknown input observer via a novel joint peak-to-peak analysis and zonotopic analysis threshold is proposed. Firstly, the disturbances are divided into decouplable and non-decouplable parts. An unknown input observer, designed with peak-to-peak performance criteria, is employed to achieve the decoupling of the decouplable disturbance part. Subsequently, based on the dynamic structure of the observer, the local non-linear part is represented as polyhedra through peak-to-peak analysis and zonotopic analysis, thereby providing an interval estimation for the system output. his method utilizes zonotopic analysis to mitigate the over-estimation issue in the initial stage inherent in traditional peak-to-peak performance-based interval estimation techniques. Furthermore, it retains the necessary degrees of freedom during the decoupling process of certain disturbances to optimize the observer's performance. Consequently, this approach significantly enhances the accuracy of the interval estimation as compared to existing methods. Moreover, the peak-to-peak performance of fault detection unknown input observer is analysed using the fuzzy basis-dependent Lyapunov function to decrease the conservatism. Lastly, simulations are analysed in order to validate the accuracy of the proposed thresholds. © 1967-2012 IEEE.

关键词： Lyapunov functions

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Railway safety under increasing speed: effect of cognitive ability on train drivers’ hazard perception

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Ergonomics 2025年 1-16页

作者： Dong, Wenli Fang, Weining Jiang, Xiaoxuan Bao, Haifeng Qiu, Hanzhao State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Haidian District Beijing China School of Automation and Intelligence Beijing Jiaotong University Haidian District Beijing China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Haidian District Beijing China

Increasing speeds in high-speed rail heighten safety concerns, particularly as foreign object detection technologies remain limited under adverse conditions. Train drivers, as the final safeguard, must accurately perceive and respond to hazards with reduced reaction margins. This study examines how four speed classes and three cognitive abilities—attention, reaction, and learning—affect hazard perception (HP) using a high-fidelity hazard perception test (HPT) developed in Unity 3D. Thirty participants completed the HPT, with performance assessed via response time and two signal detection theory indicators (sensitivity and response bias). Results show that higher speeds shorten HP response times, which seemingly indicates improved HP;however, their negative impact on sensitivity and response bias suggests reduced accuracy and cautiousness in the HP process. These findings highlight the importance of targeted cognitive training, advanced cognitive assistant systems, and adaptive cabin designs in mitigating speed-induced risks and improving safety in high-speed rail operations. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Subways

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Cepstrum Lifting and Coherent Averaging-Based Instantaneous Frequency Estimation of Low-Amplitude Signal Components for Rotating Machinery

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IEEE Sensors Journal 2025年第10期25卷 17479-17490页

作者： Liu, Shaokang Liu, Shuai Wu, Yingjie Liu, Weiliang Liu, Changliang Luo, Zhihong North China Electric Power University Control and Computer Engineering Department Beijing102200 China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Northeast Electric Power University Automation Engineering Department Jilin132000 China Inner Mongolia University of Technology Electric Power Department Hohhot010080 China

Instantaneous frequency (IF) serves multiple critical functions in the prognostic and health management of rotating machinery. The current IF estimation methodologies primarily concentrate on enhancing estimation accuracy at the algorithmic level. However, in practical industrial applications, the target signal component is often influenced by varying operating conditions, structural resonances, and background noise, causing it to become submerged or even disappear, ultimately resulting in the failure of conventional IF estimation techniques. To address these challenges, a novel IF estimation approach incorporating signal quality enhancement is developed. First, the complementary multisource vibration signal is processed by the proposed segmented cepstrum lifting (CL) method to overcome the submersion of the resonant frequency band on the target component. Then, the processed segmented signals are fused using coherent averaging (CA) technology to enhance the amplitude of the target component. Finally, leveraging the first-order approximation capability of the short-time fractional Fourier transform (STFrFT) for nonlinear frequency modulation modes, precise IF estimation is achieved. Verified by field data, the proposed method outperforms mainstream methods in both accuracy and applicability, highlighting its industrial application prospects. © 2001-2012 IEEE.

关键词： Frequency estimation

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An Enhanced Model-Free Predictive Current control for PMSMs Incorporating Discrete Space Vector Modulation 19th

An Enhanced Model-Free Predictive Current Control for PMSMs ...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yang, Yuhui Qin, Qing Qin, Yingmei Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9789819613823

Model-free predictive current control (MFPCC) has excellent parameter robustness owing to unused parameters of the controller. However, the method has the current gradient update stagnation problem due to the limited availability of only 7 voltage vectors, which results in a high current/torque ripple. To address this shortcoming, an enhanced MFPCC for permanent magnet synchronous machines (PMSMs) incorporating discrete space vector modulation (DSVM) is proposed in this paper. First, the basic voltage vectors are expanded from 7 to 37 by DSVM. Then, a novel current gradient updating method is developed to effectively reduce the current/torque ripple caused by current gradient updating stagnation. Finally, a candidate vector preselection method is designed to simplify the calculation process of current gradient updating. The effectiveness of the proposed method is verified on a 2 kW PMSM driving experimental platform. The experimental results show that the THD is reduced from 25.5% to 7.8% while the dq-axes current ripple is respectively reduced from 5.1 A and 5.8 A to 2.2A and 1.5 A. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Composite-mask GAN based on refined optical flow and disparity map for SLAM visual odometry

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Optoelectronics Letters 2025年

作者： JI Yuehui JIANG Jingwei LIU Junjie SONG Yu GAO Qiang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Application for Complex Systems

Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements， the accuracy remains limited in complex dynamic environments. In this paper， a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically， a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then， an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally， a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image， thereby increasing the overall structural constraints of the network model， improving the accuracy of camera pose estimation， and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional， supervised learning and unsupervised depth VO methods， providing better results in both pose estimation and depth estimation.

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