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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Research on Autonomous Decision-Making of Multi-UAV Air Combat Based on Deep Reinforcement Learning 7th

Research on Autonomous Decision-Making of Multi-UAV Air Comb...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhiming Qi, Hongmin Liu, Zhen Qian, Dianwei Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733354

To solve the autonomous decision-making problem of multi-UAV air combat, a multi-UAV reinforcement learning self-game autonomous decision-making method for air combat with parameter sharing is proposed. Firstly, a self-game policy pool training framework is designed. The game ability of the multi-UAV autonomous decision-making model is improved by self-game confrontation with a policy pool containing different training generations. Secondly, a parameter-sharing mechanism is proposed to train homogeneous UAVs to improve the efficiency of reinforcement learning training and the utilization of interactive data. Finally, a multi-UAV autonomous decision-making model using the multi-agent deep deterministic policy gradient algorithm is designed so that it can explore effectively in the continuous action space. In addition, experiments are designed to verify the effectiveness and migration of the designed algorithm and training framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Electromagnetic Characteristic Analysis and Design of a Novel Intensifying-Flux Hybrid Excited Permanent Magnet Machine 6

Electromagnetic Characteristic Analysis and Design of a Nove...

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6th IEEE Student Conference on Electric Machines and Systems, SCEMS 2023

作者： Lu, Ruipan Xu, Pengchong Liu, Zhangqi Liu, Xiping Jiangxi University of Science and Technology School of Electrical Engineering and Automation Jiangxi China The State University of New Jersey Department of Electrical and Computer Engineering Rutgers PiscatawayNJ United States

ISBN: (纸本)9798350329346

In this paper, a novel intensifying-flux hybrid excitation machine (IF-HEPMSM) is proposed in conjunction with the latest development requirements of electric vehicles. Therefore, the proposed IF-HEPMSM design can simultaneously obtain the synergy of strong overload capability and wide speed regulation range. Firstly, the topology, characteristics and working principle of IF-HEPMSM are introduced to reveal the improvement of the machine performance, based on which, the tooth width and yoke thickness of the machine stator are optimized to ensure the output torque of the machine while effectively improving the wide flux regulation range. The electromagnetic characteristics of the IF-HEPMSM are investigated and compared with the IF-PMSM, conventional permanent magnet machine to highlight the superiority of the IF-HEPMSM. © 2023 IEEE.

关键词： Permanent magnets

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Resilient control of Networked control Systems With Hidden DoS Attacks and Unknown Observation Probability

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IEEE Transactions on control of Network Systems 2025年第2期12卷 1313-1323页

作者： Wang, Yuan Yan, Huaicheng Park, Ju H. Zhang, Hao Shen, Hao East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China Weifang University College of Machinery and Automation Weifang261061 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of Tongji University Department of Control Science and Engineering Shanghai200092 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This paper systematically studies the problem of resilient controller design for networked control systems under imperfect communication environments. The denial of service (DoS) attacks in the case of an imperfect network are remodeled. The semi-Markov chain is introduced in this paper to provide a suitable description of random attacks. On this basis, the double-layer stochastic process is constructed, which consists of an attack sequence and an observation one. Second, since the attack modes are inaccessible, a controller is constructed based on the observation sequence by virtue of the emission probability to stabilize the system under random attacks. Due to the complexity of the network, the probability that an observer can accurately detect an attack when it occurs is considered unknown. Based on the introduced double-layer stochastic process, a mode-associated Lyapunov function is constructed to facilitate the stability analysis. Building on this foundation, sufficient conditions that can ensure the δ-error mean square stability of the closed-loop system are derived. In the end, a simulation of the unmanned ground vehicle is presented, showcasing the feasibility and validity of the proposed control scheme. © 2025 IEEE.

关键词： Stochastic systems

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Islanding Detection in DC Microgrids Based on Empirical Mode Decomposition

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IET Renewable Power Generation 2025年第1期19卷

作者： Chabok, Hengameh Zaker, Behrooz Samet, Haidar Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Improved efficiency, enhanced integration with distributed generations (DGs), and the proliferation of DC loads in power systems have driven a surge in the adoption of DC microgrids (DCMGs). However, unintentional islanding poses a significant threat to these microgrids, necessitating robust islanding detection techniques to safeguard personnel, equipment and system stability. This paper proposes two novel passive islanding detection methods for DCMGs, explicitly addressing the non-detection zone (NDZ). The first proposed method involves collecting voltage samples at the DG terminals and computing their empirical mode decomposition (EMD). Subsequently, intrinsic mode functions (IMFs) are extracted, and the energy in each window is calculated to establish a diagnosis. To effectively distinguish islanding from other disturbances and faults, various scenarios are investigated. The second method is predicated on ensemble empirical mode decomposition (EEMD). Following the collection of voltage samples from DG terminals, white noise is introduced to these samples. The method subsequently extracts IMFs through averaging and quantifies the energy content within each window. The proposed methods are validated through simulations in Simulink/MATLAB. The obtained results demonstrate the methods’ effectiveness in achieving fast and accurate islanding detection. © 2025 The Author(s). IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Islanding

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Resilient Formation control Based on Watermarks for Networked Quadrotors under Deception Attacks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 8839-8850页

作者： Zhan, Weiwei Miao, Zhiqiang Zeng, Jianxin Chen, Yanjie Wu, Zheng-Guang He, Wei Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China Fuzhou University School of Mechanical Engineering and Automation Fuzhou350108 China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Institute of Artificial Intelligence Beijing100083 China

This article addresses the problem of formation control of networked UAVs under deception attacks. A lightweight resilient formation control framework based on watermarks is proposed to achieve the desired formation configuration without extra monitoring attack status. Since deception attacks are covert, the shared information is watermarked before transmission to establish the protocol among networked UAVs. To facilitate the resilient formation controller design, a decoder is first developed to decipher the inverse transformation of deception attacks, where the watermark is the criterion for approximating the inverse transformation. The Gauss error function is introduced to eliminate some unrealistic input commands under deception attacks, obtaining the predefined input performance. Then, a resilient formation controller is developed to guarantee the global boundedness of all signals within the inner-loop and outerloop system, while the formation error converges to a small neighborhood of zero by solving the linear matrix inequality. Two challenging deception attack examples in simulations and experiments are conducted to verify the effectiveness and performance of the proposed resilient formation control framework. © 1967-2012 IEEE.

关键词： Inverse transforms

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Multiple Static Target Hunting Based on the RRT and OSPA Distance

Multiple Static Target Hunting Based on the RRT and OSPA Dis...

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2024 China automation Congress, CAC 2024

作者： Liu, Weifeng Ren, Shiwei Tang, Jiawei Su, Zijun School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi’an China School of Automation Hangzhou Dianzi University Hangzhou China University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9798350368604

This paper considers multiple static target hunting control and path planning using multi-agents. The whole hunting process can be classified into two stages. The first is cruising stage, where the agents move in a certain formation to approach the multiple static targets. Once the agents enter into certain ranges around the targets, the agents will switch into the second stage of hunting. In these two stages, it is required to consider several issues: the agent controlling, path planning, and task allocation. Specifically, the traditional multi-agent controlling algorithm, rapidly-exploring random tree (RRT), and optimal sub-pattern assignment (OSPA) are respectively used in the process. Simulation experiment shows that the path planning and hunting algorithm can efficiently realize the formation movement and multiple static targets hunting. © 2024 IEEE.

关键词： Multi agent systems

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Optimization of Cube Storage Warehouse Scheduling Using Genetic Algorithms 19

Optimization of Cube Storage Warehouse Scheduling Using Gene...

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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Ha, Won Yong Jiang, Zhong-Ping Tandon School of Engineering New York University The Control and Networks Lab Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350320695

In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time. © 2023 IEEE.

关键词： Genetic algorithms

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Equitable Client Selection in Federated Learning via Truncated Submodular Maximization 63

Equitable Client Selection in Federated Learning via Truncat...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Castillo J, Andres C. Kaya, Ege C. Ye, Lintao Hashemi, Abolfazl Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN United States Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350316339

In a typical Federated Learning paradigm, a random subset of clients is selected at every round for training. This randomly chosen subset often does not perform well when evaluated in terms of fairness as the final model's performance often varies greatly between clients. This lack of a balanced and fair performance can be detrimental in sensitive applications, such as disease diagnosis in healthcare settings. Such issues may be exacerbated by emerging performance-centric client sampling procedures. This paper proposes a new equitable client selection method, SubTrunc, that addresses the shortcomings of random selection via a modification of the well-known facility location problem through submodular function maximization. This new approach entailing submodular functions incorporates using the information of loss values of each client to ensure a more balanced and thus more fair performance of the final model. Additionally, strong theoretical guarantees on the convergence of the resulting FL algorithm are established under mild assumptions. The algorithm's performance is evaluated on heterogeneous scenarios with a clear improvement in fairness when being observed under the scope of a client dissimilarity metric. © 2024 IEEE.

关键词： Federated learning

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