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Event-Triggered Fully Distributed control: A Model-Free Adaptive Learning Algorithm

IEEE Transactions on Automatic control 2025年

作者： Ma, Yong-Sheng Che, Wei-Wei Wu, Zheng-Guang Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

This paper studies the consensus problem in multi-agent systems (MASs) under the challenge of an unknown system model and limited communication resources. A novel model-free adaptive learning algorithm is developed to learn the controller from system data. A model-based event-triggered fully distributed control (ET-FDC) algorithm is proposed to achieve consensus while saving the limited communication resources. Furthermore, a data-driven systematic learning methodology for the ET-FDC algorithm is introduced to eliminate the need for a system model. Compared with existing approaches, the main advantages of the proposed method are that it not only avoids using the system model and global topology information, but also reduces unnecessary communication. Finally, simulations are presented to illustrate the superiority of the theoretical results. © 2025 IEEE.

关键词： Contrastive Learning

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Siamese Convolutional Neural Network Based Visual Servo for Manipulator

Siamese Convolutional Neural Network Based Visual Servo for ...

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Data Driven control and Learning Systems (DDCLS)

作者： Gaofeng Deng Shan Liu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

A visual servo algorithm based on Siamese Convolution Neural Network is proposed for the manipulator to avoid the requirement of feature extraction and feature matching in the traditional image-based visual servo (IBVS). The algorithm feeds the current image and the desired image into the network at the same time, and outputs the relative pose difference between the two images. A closed-loop control system is constructed through the pose difference, and control the end-effector of the manipulator to reach the desired position to grasp the target workpiece. Meanwhile, in order to meet the large amount of data needed in training the neural network, an algorithm to automatically generate the data set is proposed, which can avoid manual collection and labeling of the data set and greatly save the cost. The simulations show the effectiveness and accuracy of the proposed method by comparing with the traditional feature point based IBVS, and the grasping experiment shows the feasibility of the proposed method in actual practice.

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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Blast furnace state monitoring method based on reliability analysis of sensing system

Blast furnace state monitoring method based on reliability a...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Yidong Li Yikun Jiang Xin Wang Jing Zhou Xiao-Yu Tang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

Blast furnace (BF) ironmaking is a complex process with a series of devices, and failures occur from time to time. The accurate BF state monitoring is necessary and can effectively reduce the waste of resource and the occurrence of safety accidents. Currently, most data-driven BF state monitoring methods directly use the industrial field data collected by sensors, while less attention has been paid to the reliability of the data. This will reduce the accuracy of BF state monitoring, since data anomalies caused by kinds of sensor failures will directly lead to erroneous judgments of BF fault state. To address the above issues, this paper proposes a novel BF state monitoring method which takes the sensor states (failure or not) into account. In this method, an initial BF state monitoring model and a sensor state monitoring model are respectively constructed, mainly based on back propagation neural networks (BPNN). Afterwards, an online model is developed to coordinate the logical relationship between the initial BF state results and the sensor state results, to provide the final judgment of BF state. Validations on the dataset collected from a practical BF has shown that the BF state monitoring results is with an overall accuracy rate of 96.91%.

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A New Estimator for the Number of High-Dimensional Signals Using Ratio of Higher-Order Moments

A New Estimator for the Number of High-Dimensional Signals U...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Lewen Rong, Yao Tang, Mengjiao Du, Xiaolin Li, Fan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China School of Computer and Control Engineering Yantai University Yantai China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781839539954

This paper provides a novel estimation procedure for determining the number of high-dimensional complex-valued signals embedded in Gaussian white noise. Initially, this problem is formulated as a sequence of nested hypothesis tests. To enhance the decision performance, we introduce new test statistics based on the ratio of the fourth- and square second-order moments of the population covariance eigenvalues. This enables us to leverage higher-order moments information in the observed data, distinguishing it from conventional tests that rely solely on the first and second order moments. In the context of high-dimensional, complex-valued data and within a sub-sphericity testing framework, unbiased and consistent estimates are derived for these two higher-order moments. Subsequently, we conduct an asymptotic analysis to incoporate these estimates into the construction of the test statistics and the design of our proposed estimation scheme for determining the number of sources. Finally, numerical examples are presented to show its effectiveness and superiority over several classical estimation methods. © The Institution of engineering & Technology 2023.

关键词： White noise

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Direct visualisation of metal-defect cooperative catalysis in Ru-doped defective MOF-808

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Journal of Materials Chemistry A 2024年第30期12卷 19018-19028页

作者： Xu, Chirui Orbell, William Wang, Guilian Li, Boye Ng, Bryan K. Y. Wu, Tai-Sing Soo, Yun-Liang Luan, Zhao-Xue Tang, Kangjian Wu, Xin-Ping Tsang, S. C. Edman Zhao, Pu Innovation Center for Chemical Science College of Chemistry Chemical Engineering and Materials Science Soochow University Suzhou215123 China Department of Chemistry University of Oxford OxfordOX1 3QR United Kingdom State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China National Synchrotron Radiation Research Center Hsinchu30076 Taiwan Department of Physics National Tsing Hua University Hsin-chu30013 Taiwan State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Centre for Computational Chemistry Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai200237 China

Improving the efficiency of catalytic materials is vital to the chemical and energy industries. Constructing neighbouring active sites in metal-organic framework (MOF) materials for cooperative catalysis is a promising way to achieve the above goal. However, it is difficult to fine-tune active sites at the atomic level due to the challenge of visualising their local structures and their interaction with substrates. In this article, we report the direct visualisation of metal and defect active sites and binding of the phenol substrate in Ru-doped defective MOF-808. X-ray absorption spectroscopy, X-ray pair distribution function analysis, X-ray powder diffraction, and infrared spectroscopy reveal that the enhanced selective hydrogenation originates from the specific adsorption geometry of phenol over 7-centred Ru clusters and hydroxyl or water of defect sites. This mechanism also well explains the high catalytic activity in CO2 reduction. This work represents the first example of structural elucidation of metal-defect cooperative catalysis in MOFs and will lead to the rational design of new superactive MOF catalysts. © 2024 The Royal Society of Chemistry.

关键词： Substrates

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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An Improved NSGAII Algorithm for Flexible Job-shop Scheduling Problem Based on A New Decoding Mechanism

An Improved NSGAII Algorithm for Flexible Job-shop Schedulin...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Man Li Kai Ma Shiliang Guo Shuai Li Bin Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

The flexible job-shop scheduling problem (FJSP) is a famous combinatorial optimization problem. FJSP is widely used in process manufacturing industry, so it is of great significance to study FJSP to improve production efficiency. In this paper, a non-dominated sorting genetic algorithm II based on a new decoding mechanism (NSGAII_NDM) is proposed. Firstly, the opposite-based learning strategy is introduced on the basis of NSGAII, and the sufficiency of global search is improved by generating the opposite value. Secondly, an improved adaptive crossover and mutation probability strategy is added, which can dynamically adjust the inherent crossover and mutation probability with the number of iterations and improve the convergence speed of the algorithm. Furthermore, based on fully active scheduling strategy, a new decoding mechanism is proposed, which can get a better scheduling scheme by arranging the machines and starting time of each process more reasonably. Finally, the improved algorithm is tested in a standard test case, which verifies the superiority of NSGAII_NDM in solving the flexible job-shop scheduling problem.

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Suitability Analysis on Site in Mountainous Areas-A Case Study of Panzhihua Region 2

Suitability Analysis on Site in Mountainous Areas-A Case Stu...

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2nd International Conference on Geographic Information and Remote Sensing Technology, GIRST 2023

作者： Zhou, Fang-Xiao School of Mathematics and Computer Science Panzhihua University Panzhihua China Sichuan Province Key Laboratory of Higher Education Institutions for Comprehensive Development and Utilization of Industrial Solid Waste in Civil Engineering Panzhihua China

ISBN: (纸本)9781510668478

Site selection is a complex task, especially in mountainous areas, where more factors about estimating site suitability need to be considered. The choose of evaluation indicators and the setting of indicator weights have a crucial impact on the site selection results. Panzhihua city has typical mountainous characteristics, from which some villages are drawn as the research data. Firstly, eight evaluation indicators are screened out through literature analysis;then the indicator values of these villages or towns were calculated using GIS method and statistical method. Decision tree model is adopted for evaluation suitability level of each village in this paper. Unlike those methods of directly calculating the evaluation score, the indicator weights in the decision tree model needs to be learned in advance, and then new candidate sites can be estimated by these learned rules. In addition, a new method is proposed for computing the pair-wise matrix, in that the gini coefficient is used to calculate the relative weights of two indicators each other. Through the test, the recall and precision of the trained model are 0.89. This model is also used to predict new sites. © 2023 SPIE.

关键词： Decision trees

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