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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication *** transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear *** the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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FPGA-based Digital Implementation of Flexible Power Control for Three-phase to Single-phase MMC-based Advanced Co-phase Traction Power Supply System

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 2015-2027页

作者： Jiangpeng Yang Hongjian Lin Haotian Zhu Zeliang Shu Lan Ma Changsong Cai Pengcheng Zhang Li He Ruoyu Li Shasha Song Josep M.Guerrero National Rail Transportation Electrification and Automation Engineering Technology Research Center Chengdu 611756China School of Electrical Engineering Southwest Jiaotong UniversityChengdu 611756China IEEE Department of Electrical Engineering City University of Hong KongHong KongChina School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Department of Energy Technology Aalborg UniversityAalborg 9220Denmark

A three-phase to single-phase modular multilevel converter based advanced co-phase traction power supply(MMC-ACTPS) system is an effective structure to address the concerns of phase splitting and poor power quality of the conventional electrified railway. Due to the large number of MMCACTPS system modules, I/O resources and computing speed have high requirements on processors. Moreover, the module capacitor balance is challenging because the sorting time is too long when the traditional sorting algorithm for voltage balance is used. To solve the above issues, a digital implementation scheme of flexible power control strategy for three-phase to single-phase MMC-ACTPS system based on field programmable gate array(FPGA), which has sufficient I/O resources, has been proposed. Due to the parallel execution characteristics of the FPGA, the execution time of the controller and the modulator can be greatly reduced compared with a digital signal processor(DSP) + FPGA or DSpace. In addition, an improved sorting algorithm is proposed to reduce the sorting time and the implementation steps are analyzed. Finally, simulation and experimental results are presented to demonstrate the effectiveness and correctness of the proposed control strategy.

关键词： Advanced co-phase power traction supply(ACTPS)system modular multilevel converter(MMC) digital implementation flexible power transmission circulating current mitigation

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Short-term Load Forecasting of an Integrated Energy System Based on STL-CPLE with Multitask Learning

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Protection and Control of Modern Power Systems 2024年第6期9卷 71-92页

作者： Suxun Zhu Hengrui Ma Laijun Chen Bo Wang Hongxia Wang Xiaozhu Li Wenzhong Gao the College of Energy and Electrical En-gineering Qinghai UniversityXining 810016China the School of Electrical and Automation Wuhan UniversityWuhan 430072China the Department of Electrical and Computer Engineering University of DenverDenver CO 80208USA Engineering Research Center for Renewa-ble Energy Generation and Grid Connection Technology Ministry of EducationXinjiang UniversityUrumchi 830046China

Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among *** line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called ***,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual ***,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature *** approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.

关键词： Integrated energy system multienergy load forecasting convolutional progressive layer extrac-tion network seasonal-trend decomposition.

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Privacy of Distributed Optimality Schemes in Power Networks

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IEEE Transactions on Smart Grid 2023年第5期14卷 4021-4034页

作者： Kasis, Andreas Khan, Kanwal Polycarpou, Marios M. Timotheou, Stelios University of Cyprus KIOS Research and Innovation Center of Excellence The Department of Electrical and Computer Engineering Nicosia1678 Cyprus

The increasing participation of local generation and controllable demand units within the power network motivates the use of distributed schemes for their control. Simultaneously, it raises two issues;achieving an optimal power allocation among these units, and securing the privacy of the generation/demand profiles. This study considers the problem of designing distributed optimality schemes that preserve the privacy of the generation and controllable demand units within the secondary frequency control timeframe. We propose a consensus scheme that includes the generation/demand profiles within its dynamics, keeping this information private when knowledge of its internal dynamics is not available. However, the prosumption profiles may be inferred using knowledge of its internal model. We resolve this by proposing a privacy-preserving scheme which ensures that the generation/demand cannot be inferred from the communicated signals. For both proposed schemes, we provide analytic stability, optimality and privacy guarantees and show that the secondary frequency control objectives are satisfied. The presented schemes are distributed, locally verifiable and applicable to arbitrary network topologies. Our analytic results are verified with simulations on a 140-bus system, where we demonstrate that the proposed schemes offer enhanced privacy properties, enable an optimal power allocation and preserve the stability of the power network. © 2010-2012 IEEE.

关键词： Smart power grids

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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IEEE Transactions on automation Science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 898-915页

作者： Kangkang Zhang Christodoulos Keliris Thomas Parisini Bin Jiang Marios M.Polycarpou Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUKand also with the KIOS Research and Innovation Center of ExcellenceUniversity of CyprusNicosia 1678Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of CyprusNicosia 1678Cyprus Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUK Department of Engineering and Architecture University of TriesteTrieste 34127Italy KIOS Research and Innovation Center of Excellence University of CyprusNicosia 1678Cyprus College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyberphysical systems subject to process disturbances and measurement noise.A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack *** stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state *** order to help detect such attacks,a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change,due to the attack,at a time prior to the attack occurrence time.A key characteristic of this residual is that its magnitude increases every time a new attack *** estimate this unknown residual,an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting *** smoother design guarantees robustness with respect to process disturbances and measurement noise,and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting *** adaptive threshold is designed based on the estimated backward-in-time residual,and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed ***,a simulation example is used to demonstrate the effectiveness of the developed methodology.

关键词： Backward-in-time equivalent quantity fixed-point smoother intermittent integrity attacks

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PS-IPS: Deploying Intrusion Prevention System with machine learning on programmable switch

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Future Generation computer Systems 2024年 152卷 333-342页

作者： Lee, Alan Y.-P. Wang, Michael I.-C. Hung, Chi-Hsiang Wen, Charles H.-P. Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering New York University Tandon School of Engineering United States Advanced SDN Technology Research Center National Yang Ming Chiao Tung University Taiwan

Intrusion prevention is significant to avoid device damage and financial losses. researchers have proposed various Intrusion Prevention Systems (IPS) to prevent malware, including traditional and SDN-based IPS. However, existing IPSs suffer from low throughput problems caused by detection and rule-installation delays. Here, we propose a programmable switch-base IPS (named PS-IPS), which utilizes the switch CPU and pipeline to detect malware. PS-IPS consists of four main components: (1) parser, (2) flow filter, (3) recirculation director, and (4) malware detector. According to the experiment, PS-IPS achieves a 183X throughput than the SDN-based IPS. The response time of PS-IPS is also reduced by 99.99%, showing that PS-IPS effectively prevents malware with a single programmable switch. © 2023

关键词： Machine learning

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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SE-CNN Attention Structure for Quantitative EEG-Based Assessment of VR Motion Sickness 23

SE-CNN Attention Structure for Quantitative EEG-Based Assess...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Xu, Mengdie Yang, Banghua Liu, Mutian Huang, Yiling Xia, Xinxing Zhang, Jie Shanghai University School of Mechanical and Electrical Engineering and Automation Research Center of Brain-Computer Engineering Shanghai China

ISBN: (纸本)9781450399029

The applications of virtual reality (VR) technology are currently numerous and promising, but motion sickness (MS) problems are affecting the development of the VR market. Questionnaires are commonly used to subjectively assess motion sickness, but they are applied before and after the user experiences VR and cannot assess the user’s motion sickness in real time. In this work, this paper proposes a convolutional neural network (CNN) structure incorporating squeeze and stimulus (SE) attention mechanisms, with subjective questionnaire scores used as markers of electroencephalographic signal (EEG) for real-time prediction of VR motion sickness scores. In this thesis, EEG signals were collected from 16 subjects in a virtual reality environment and the experimental data were fed into the network structure to train the model. The experimental results showed that the root mean square error (RMSE) was 25.69, the Pearson linear correlation coefficient (PLCC) was greater than 0.85, and the Spearman rank correlation coefficient (SROCC) was also greater than 0.85, indicating a significant relationship between the prediction and subjective assessment scores (p © 2023 Copyright held by the owner/author(s).

关键词： Virtual reality

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