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7th International Conference on Electric Power Equipment - Switching Technology, ICEPE-ST 2024

作者： Wu, Zhiwei Chen, Li'an Guo, Mengqian School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China

ISBN: (纸本)9798350388947

Electromagnetic potential transformer(PT) may undergo ferroresonance when subjected to external disturbances, which causes overvoltage and affects the stable operation of the power system. In order to provide reference advice for the installation and monitoring of the sensor, the temperature field of 10kV electromagnetic PT was simulated by utilizing the finite element method. In this paper, after establishing a three-dimensional model, joule loss, eddy current loss and hysteresis loss were calculated as heat sources, and then the temperature field distribution was simulated. Subsequently, the differences before and after the ferroresonance were compared to analyse the influence of the ferro resonance and inferred the location where the abnormal temperature rise occurs. The simulation results showed that the highest temperature of the shell is located at the side closest to the heat source, where the epoxy resin is the thinnest and most sensitive to temperature changes. Therefore, the temperature sensor should be mounted at this location, where the abnormal temperature rise can be detected quickly. This study not only will provide suggestions for the installation and monitoring of sensors, but also will lay a foundation for the realization of the 'self-healing' function of the robust smart grid, and improve the reliability and security of the grid operation. © 2024 IEEE.

关键词： Electric power transmission networks

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

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Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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Distributed formation control of multiple aerial vehicles based on guidance route

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Chinese Journal of Aeronautics 2023年第3期36卷 368-381页

作者： Jinyong CHEN Rui ZHOU Guibin SUN Qingwei LI Ning ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007China AVIC Xi’an Flight Automatic Control Research Institute Xi’an 710076China

Formation control of fixed-wing aerial vehicles is an important yet rarely addressed problem because of their complex dynamics and various motion constraints,such as nonholonomic and velocity *** guidance-route-based strategy has been demonstrated to be applicable to fixed-wing ***,it requires a global coordinator and there exists control lag,due to its own *** this reason,this paper presents a fully distributed guidance-route-based formation approach to address the aforementioned ***,a hop-count scheme is introduced to achieve distributed implementation,in which each aircraft chooses a neighbor with the minimum hop-count as a reference to generate its guidance route using only local ***,the model predictive control algorithm is employed to eliminate the control lag and achieve precise formation shape *** addition,the stall protection and collision avoidance are also ***,three numerical simulations demonstrate that our proposed approach can implement precise formation shape control of fixed-wing aircraft in a fully distributed manner.

关键词： Formation shape control Fixed-wing aircraft Guidance route Hop-count estimation Model predictive 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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An Overview of Finite/Fixed-Time control and Its Application in engineering Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2106-2120页

作者： Yang Liu Hongyi Li Zongyu Zuo Xiaodi Li Renquan Lu IEEE College of Automation and Electronics Engineering Qingdao University of Science and TechnologyQingdao 266061 School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Automation Guangdong University of Technologyand also with the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhou 510006China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China School of Mathematics and Statistics Shandong Normal UniversityBeijing 100083China Center for Control and Engineering Computation Shandong Normal UniversityJinan 250014China

The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability,the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial *** work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.

关键词： Adding a power integrator finite/fixed-time control and application homogeneous theory sliding mode control

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Observer-Based SMC for Discrete Interval Type-2 Fuzzy Semi-Markov Jump Models

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1913-1925页

作者： Qi, Wenhai Li, Runkun Shi, Peng Zong, Guangdeng Qufu Normal University School of Engineering Rizhao276826 China The University of Adelaide School of Electrical and Mechanical Engineering AdelaideSA5005 Australia Obuda University Research and Innovation Centre Budapest1034 Hungary Tiangong University School of Control Science and Engineering Tianjin300387 China Weifang University College of Machinery and Automation Weifang261061 China

This study is devoted to the observer-based sliding mode control (SMC) for discrete nonlinear semi-Markov jump models with incomplete sojourn information and application to the quarter-car suspension model. The nonlinear plant with parameter uncertainty is represented by an interval type-2 fuzzy model, where the membership functions of the fuzzy rules are related to the system mode. Since sojourn information is challenging to obtain in practice, the sojourn time probability density function is considered to be incompletely available. The considered system is more general, not only relaxing the traditional assumption that all the sojourn time probability density functions are completely available, but also covering completely available sojourn time probability density functions as a special case. The main innovation is that an observer-based SMC scheme is designed, which makes the discrete nonlinear models have better dynamic performance, and realizes the reachability of discrete quasi-sliding mode. By the interval type-2 fuzzy and classical Lyapunov function, the mean-square stability criterion of the semi-Markov jump models is constructed employing additional matrix variables. Then, an observer-based SMC mechanism is constructed to achieve the reachability of the quasi-sliding mode. Ultimately, the proposed method is validated by a two-degree-freedom quarter-car suspension model. © 1993-2012 IEEE.

关键词： Lyapunov functions

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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 Tracking control for Leader-Following Consensus of Nonlinear Multiagent Systems

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3098-3107页

作者： Su, Wei Mu, Chaoxu Yang, Xiong Bian, Jinshan Sun, Changyin Tianjin University School of Control Science and Engineering Tianjin300072 China Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Hefei230601 China Anhui University School of Artificial Intelligence Hefei230000 China Southeast University School of Automation Nanjing210096 China

This article investigates distributed adaptive leader-following consensus tracking optimal control problem for nonlinear multiagent systems (MASs) subject to unknown nonlinearities and uncertain external disturbances. In contrast to traditional centralized control, the primary challenge is that only partial subsystems can access the desired reference trajectory. To address this, positive time-varying smooth function compensating terms are introduced to counteract the effects of uncertain external disturbances and unknown desired trajectories. Then, by fusing consensus errors into the backstepping technique, feedforward controllers are given. On this basis, the controlled nonlinear systems are transformed into an equivalent affine form, and feedback optimal controllers are designed using actor and critic neural networks (NNs) to execute control behavior and evaluate control performance. The whole control laws comprise both feedforward and feedback controllers. The proposed distributed adaptive consensus control protocol can simultaneously achieve desired optimal control performance and minimize the cost function, as demonstrated through theoretical analysis and simulation results. © 2025 IEEE.

关键词： Cost functions

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An adaptive large neighborhood search for the multi-point dynamic aggregation problem

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control Theory and Technology 2024年第3期22卷 360-378页

作者： Shengyu Lu Bin Xin Jie Chen Miao Guo School of Automation Beijing Institute of TechnologyBeijing 100081China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

The multi-point dynamic aggregation(MPDA)problem is a challenging real-world *** the MPDA problem,the demands of tasks keep changing with their inherent incremental rates,while a heterogeneous robot fleet is required to travel between these tasks to change the time-varying state of each *** robots are allowed to collaborate on the same task or work separately until all tasks are *** is challenging to generate an effective task execution plan due to the tight coupling between robots abilities and tasks'incremental rates,and the complexity of robot *** effectiveness consideration,we use the variable length encoding to avoid redundancy in the solution *** creatively use the adaptive large neighborhood search(ALNS)framework to solve the MPDA *** the proposed algorithm,high-quality initial solutions are generated through multiple problem-specific solution construction *** heuristics are also used to fix the broken solution in the novel integrated decoding-construction repair process of the ALNS *** results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed ALNS can obtain better task execution plans than some state-of-the-art algorithms in most MPDA instances.

关键词： Adaptive large neighborhood search(ALNS) Multi-point dynamic aggregation(MPDA) Heuristic solution construction Multi-robot collaboration

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