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SSRN 2024年

作者： Gao, Hongbo Yang, Hanqing Zhu, Juping Su, Huiping Wang, Ning Xie, Liping Li, Keqiang Department of Automation University of Science and Technology of China Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Chongqing College of Mobile Communication Chongqing400044 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving information obtained;however, driving risks are difficult to assess due to many influencing factors and great uncertainty. The risk entropy model of vehicles is visualized by introducing the concept of basic isotropic risk elliptic line (BIREL). Further incorporating the concept of information entropy, risk entropy models for vehicle interaction and vulnerable participants are established, and the risk entropy model for roads is developed using raster coordinates. Ultimately, a three-dimensional driving risk entropy model is constructed based on the human-vehicle-road coupling model, with appropriate weights assigned to each parameter through the DDPG algorithm. Experimental results demonstrate the effectiveness of the driving risk entropy model. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optimizing PV-battery energy storage for netzero emission traction power system through artificial rabbit optimization method

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Journal of Energy Storage 2025年 126卷

作者： Li, Zhijian Hu, Yihua Ollerenshaw, Richard Li, Xinian He, Zengxiang Esfahani, Mohammad Nasr School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom Department of Engineering King's College London LondonWC2R 2LS United Kingdom Siemens Rail Electrification Siemens Mobility York United Kingdom School of Information and Electronics Engineering Shandong Technology and Business University Yantai264005 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

The transition from traditional fossil fuels to renewable energy is becoming increasingly feasible and is essential for addressing energy and environmental challenges. In transportation electrification, the variability of power demand and the intermittent nature of renewable energy generation present challenges to achieving high renewable energy utilization. Battery energy storage systems (BESS) integrated to renewable resources offer a viable solution to these intermittency issues, though their costs require careful optimization. This paper explores the cost implications of using on-site renewable energy to power a railway traction system. It proposes a photovoltaic (PV) energy storage strategy optimized with the Artificial Rabbit Algorithm (ARO). By applying railway load and regional PV data, we optimize the capacity of BESS and the rated power of the PV site to meet railway electricity demands while minimizing investment and operational costs. The study shows that the capacity of BESS and the size of the PV installation depend on different self-sufficiency ratio (SSR) requirements, leading to an optimized configuration for highly renewable systems. Additionally, the study highlights that considering battery aging is crucial for redu.ing costs in traction power systems using renewable energy and remains economically viable under time-of-use pricing policies. © 2025 Elsevier Ltd

关键词： Battery storage

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A Robust Rail Positioning Method for Turnout and Non-turnout Sections

A Robust Rail Positioning Method for Turnout and Non-turnout...

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第40届中国控制会议

作者： Shixiang Su Shuzhen Tong Xiaobo Lu Yun Wei School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Beijing Mass Transit Railway Operation Corporation Limited

Rail positioning is an essential task for railway track defect detection. The turnout is one of the main components of railway tracks. In order to realize the positioning of rails in turnout sections and non-turnout sections, this paper proposes a robust rail positioning method based on double sliding windows. Firstly, extract lines from the preprocessed image. Secondly,lines are discretized into points, and then all points are projected to the x-axis of the image. Finally, a double sliding window method is used to search for the rail area. The experimental results show that the proposed method can effectively identify the rail’s location in the turnout area, and the detection accuracy can reach 99%. What’s more, it has strong adaptability to complex detection environments such as curved rail or broken rail.

关键词： Rail positioning Turnout Railway Line detection Sliding window

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Few-data guided learning upon end-to-end point cloud network for 3D face recognition

arXiv

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arXiv 2021年

作者： Yu, Yi Da, Feipeng Zhang, Ziyu School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

3D face recognition has shown its potential in many application scenarios. Among numerous 3D face recognition methods, deep-learning-based methods have developed vigorously in recent years. In this paper, an end-to-end deep learning network entitled Sur3dNet-Face for point-cloud-based 3D face recognition is proposed. The network uses PointNet as the backbone, which is a successful point cloud classification solution but does not work properly in face recognition. Supplemented with modifications in network architecture and a few-data guided learning framework based on Gaussian process morphable model, the backbone is successfully modified for 3D face recognition. Different from existing methods training with a large amount of data in multiple datasets, our method uses Spring2003 subset of FRGC v2.0 for training which contains only 943 facial scans, and the network is well trained with the guidance of such a small amount of real data. Without fine-tuning on the test set, the Rank-1 Recognition Rate (RR1) is achieved as follows: 98.85% on FRGC v2.0 dataset and 99.33% on Bosphorus dataset, which proves the effectiveness and the potentiality of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Finite-Time Coordinated Path Following control of Leader-Following Multi-Agent systems

Research Square

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Research Square 2021年

作者： Chen, Weibin Chen, Yang-Yang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

The applications of the continuous feedback method to achieve both path following and a formation moving along desired orbits at a finite time is presented. It is assumed that the topology among the virtual leader and the followers is directed. An additional condition of so called barrier function to yield all the agents moving within a limited area is designed. A novel continuous finite‑time path following control law is first designed based on the barrier function and backstepping. Then a novel continuous finite‑time formation algorithm is designed by regarding the path following errors as disturbances. The settling time properties of the resulting system are studied in detail. Simulations are presented to validate the proposed strategies. © 2021, CC BY.

关键词： Multi agent systems

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Model-Based Prediction of Grinding Surface Roughness With Error Correction via a Knowledge-Based Fuzzy Broad Learning System

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IEEE Transactions on Instrumentation and measurement 2025年 74卷

作者： Zewen Hu Hongcai Chen Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

Surface roughness is a key indicator of product quality. Traditional measurement methods generally rely on manual trials and inspections, which are time-consuming and costly. Therefore, developing an accurate and reliable prediction model is vital for automatic grinding, as it enables more informed decision-making, redu.es waste, and improves efficiency. However, current model- and data-driven methods suffer from either low accuracy or poor interpretability. To address these challenges, this article proposes an enhanced hybrid framework that integrates a mechanistic model-based predictor with a knowledge-based fuzzy broad learning system (KBFBLS) for error correction. The mechanistic model offers a physically interpretable baseline estimate of surface roughness, while KBFBLS enhances prediction accuracy by learning the mapping from process parameters to the residual errors of the mechanistic model. Built upon the fuzzy broad learning system (FBLS)—an emerging neuro-fuzzy model for efficient nonlinear modeling—KBFBLS integrates expert knowledge-guided fuzzy partition and variance-determined Gaussian membership function (MF) widths, two novel strategies that further improve the system’s expressiveness and adaptability, making it a powerful error corrector. Experimental results on real-world robotic disk grinding tasks show that the proposed framework outperforms representative model-driven, data-driven, and hybrid methods. Furthermore, its adaptability to other machining processes is validated using the wheel grinding and milling datasets.

关键词： Surface roughness Rough surfaces Surface treatment Predictive models Adaptation models Learning systems Training Accuracy Milling Error correction

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RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation measurement and Classification for ISAC Applications

arXiv

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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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Modulated-Virtual-Vector-Based Predictive Current control for Dual Three-Phase PMSM With Enhanced Steady-State Performance

Modulated-Virtual-Vector-Based Predictive Current Control fo...

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Annual Conference of Industrial Electronics Society

作者： Ze Li Xingyu Zheng Jinhui Xia Liling Wang Yuanbo Guo Xiaonan Gao Zhiheng Liu Xiaohua Zhang College of Electronic and Information Engineering Hebei University Baoding China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Southeast University Nanjing China School of Electrical Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Electrical Engineering Dalian University of Technology Dalian China

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

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Output feedback based high-order sliding mode control design of electrohydraulic system using an exact differentiator

Output feedback based high-order sliding mode control design...

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control Technology and Applications (CCTA),

作者： Hao Sun Zongxuan Sun Shihua Li Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing PR China University of Minnesota-Twin Cities Minneapolis Minnesota USA

ISBN: (纸本)9781665436441

Electrohydraulic systems, which have been widely used in a broad range of applications, are a typical nonlinear system with uncertainties. The presence of nonlinearities, parameter variations degrades the performance of linear controllers, such as the PID controller. As one of the most robust control strategies, sliding mode control methods attract the attention of researchers and are applied to electrohydraulic systems. However, for many applications, only the piston position measurement information is available. Owing to the existence of the nonlinear orifice equation, the observer design is a challenging task and satisfactory performance is not guaranteed in the existing literature. Therefore, an output feedback based high-order sliding mode control (HOSMC) design of electrohydraulic systems is proposed in this paper. By using an exact differentiator, the derivatives of the tracking error are obtained and used to construct the HOSMC. Compared to the conventional sliding mode control (SMC), the HOSMC can achieve a finite-time convergence and improved tracking accuracy. To verify the tracking performance, a simulation is performed on a camless engine valve actuation system.

关键词： Robust control Uncertainty Switching frequency Simulation Position measurement Valves Electrohydraulics

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Cooperative control Based on Distributed Attack Identification and Isolation

Cooperative Control Based on Distributed Attack Identificati...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Hailing Liu Dalin Huang Ya Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

This paper studies the secure consensus control of multi-agent systems where some agents are injected into malicious attacks and compromised. We focus on the distributed detection and identification of attacks on agents and the realization of consensus of agents against attacks. Firstly, we design monitors that can perform precise distributed attack detection. Then, we propose a distributed attack identification algorithm by constructing distributed observers. After that, a cooperative controller is proposed which ensures the normal control of systems by means of isolating the agents which are attacked. Finally, we validate our results through simulation examples.

关键词： Multi-agent systems Monitoring Sensors Sensor systems Security Observers Data models

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