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Design of De-tumbling Device for Improving the De-tumbling Performance of Uncooperative Space Target

Space(Science & technology) 2024年第1期4卷 10-19页

作者： Lei Du Zhen Chen Hengzai Hu Xiangdong Liu Youguang Guo School of Automation Beijing Institute of TechnologyBeijingChina Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information TechnologyBeijingChina School of Electrical and Data Engineering University of Technology SydneySydneyAustralia

This article presents the design of an optimal coil structure for 2 de-tumbling devices, each is carried by a de-tumbling robot. The design is based on electromagnetic eddy current method and aims to reduce the angular velocity of uncooperative space targets. It proposes an optimization framework with the advantages of safety and high performance. The magnetic field analytical model is established by the designed coil’s structure parameters, and the optimal structure parameters of the coil are determined. To further ensure the maximum magnetic field at the target, the electromagnetic characteristics under different current directions in the 2 coils are analyzed based on magnetic field analytical model, and their accuracy is verified using finite element method (FEM). Additionally, an improved Maxwell’s stress tensor method is proposed to calculate the de-tumbling torque, and its accuracy is assessed using traditional Maxwell’s stress tensor and virtual displacement method. The proposed optimal coil structure and its optimization framework can de-tumble over 1 million targets of various sizes, demonstrating universality.

关键词： uncooperative space target optimal structure parameters detumbling optimization framework electromagnetic eddy current reduce angular velocity electromagnetic eddy current method coil structure

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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Influence of university campus spatial morphology on outdoor thermal environment: A case study from Eastern China

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Energy and Built Environment 2025年第1期6卷 43-56页

作者： Qi, Yansu Chen, Lan Xu, Jiuzhe Liu, Chao Gao, Weijun Miao, Sheng Innovation Institute for Sustainable Maritime Architecture Research and Technology Qingdao University of Technology Qingdao266033 China Faculty of Environmental Engineering The University of Kitakyushu Kitakyushu808-0135 Japan School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266033 China School of Information and Control Engineering Qingdao University of Technology Qingdao266033 China

Outdoor thermal comfort is crucial for creating sustainable and livable urban spaces. Studying the distribution and impact factors of the thermal environment can provide theoretical support for improving the thermal environment and spatial planning. This study investigates the influence of well-defined boundary spaces on outdoor thermal comfort and proposes design measures to improve spatial thermal comfort. High-resolution data obtained from unmanned aerial vehicles (UAVs) is integrated into outdoor microclimate simulations to enhance the accuracy and precision of the models. The Physiological Equivalent Temperature (PET) index is employed as an evaluation indicator, considering the categorization of PET values into different comfort levels. Additionally, the axial evaluation method is introduced to assess thermal comfort more accurately, reflecting the perceived thermal comfort by individuals. Through a case study in a mixed-use and clear boundary area, the research identifies the variables that influence outdoor thermal comfort and provides design guidelines to enhance spatial thermal comfort. The correlations between the spatial morphology index and PET were analyzed by multiple regression. The findings contribute to the understanding of outdoor thermal comfort in complex urban environments and offer valuable insights for the design and planning of comfortable and sustainable outdoor spaces. © 2023

关键词： Thermal comfort

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Class Incremental Learning Method for Hyperspectral Images Based on Real Data Playback Mechanism and Classification Network Optimization 15th

Class Incremental Learning Method for Hyperspectral Images B...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Liu, Xiaodong Wang, Siqi Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Computer Science and Technology Information Countermeasure Technique Institute Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application requirements, new classes of HSI continue to emerge. In the face of open and dynamic application scenarios, the model is required to be able to continuously learn new categories based on maintaining existing category knowledge. Class incremental learning algorithms have received extensive attention as key solutions to the problem above. In this paper, we use a regularization-based incremental learning algorithm, elastic weight consolidation (EWC) for class incremental learning for HSI classification. After data preprocessing to reduce data dimensions, we select new classes for research on class incremental learning. We combine 3D-2D convolutional structures and residual blocks as the classification network and use real data playback mechanism to improve class recognition accuracy and realize class incremental recognition of HSI data. We demonstrate the feasibility of the algorithm with extensive experiments based on three widely used hyperspectral datasets. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Pango FPGA Implementation of a Real-Time Display System for Multi-Channel Video Processing

Pango FPGA Implementation of a Real-Time Display System for ...

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2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2024

作者： Huang, Cao Du, Lifu Peng, Yu Xiang, Gang Yu, Yang Chen, Jingyi School of Electronics Information Engineering Habin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China Beijing Aerospace Automatic Control Institute Department of System Engineering Beijing China

ISBN: (纸本)9798350388855

A Pango FPGA-based solution for merging and processing multiple independent video streams and reconstructing a real-time display system is presented. The system handles each video stream individually, then produces a combined video output in HDMI format. To ensure high efficiency and low latency, a parallel processing scheme and a hardware-oriented architecture are employed. The Pango PG50K FPGA board acts as the central hardware for managing inputs, reconstructing video, and producing outputs. Rapid-access DDR3-SDRAM ensures efficient data caching and signal integrity. This system is ideal for applications requiring rapid response, such as traffic monitoring and industrial automation, and supports quick algorithm iteration and optimization, enhancing performance and adaptability. © 2024 IEEE.

关键词： Video streaming

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A Study on the Thermal Deformation Prediction Model of a Motorized Spindle Based on Swarm Intelligence Optimization Algorithms 7

A Study on the Thermal Deformation Prediction Model of a Mot...

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7th IEEE International Conference on information Systems and Computer Aided Education, ICISCAE 2024

作者： Wei, Wei Wang, Ruizhe Department of Information and Control Engineering Shenyang Institute of Science and Technology Shenyang China School of Electrical Engineering Suihua University Suihua China

ISBN: (纸本)9798350350760

Thermal deformation of the motorized spindle significantly impacts the machining accuracy of CNC machine tools. The thermal expansion coefficient, a critical parameter affecting thermal deformation, is highly nonlinear, making it challenging to determine an accurate coefficient that enhances machining precision. This study proposes a thermal deformation model for the motorized spindle based on an improved Grasshopper Optimization Algorithm, aimed at optimizing the thermal expansion coefficient. Experimental studies were conducted on the motorized spindle at various rotational speeds. The results demonstrate that the thermal expansion coefficient obtained using the improved Grasshopper Optimization Algorithm yields more accurate predictions of thermal deformation, with smaller errors compared to actual measurements, than those obtained using an empirical thermal expansion coefficient. ©2024 IEEE.

关键词： Swarm intelligence

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Configuration Optimization Design of Wind Solar Complementary PG(WSCPG) System Based on Grey Prediction PID

Configuration Optimization Design of Wind Solar Complementar...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Gu, Bo Shenyang Institute Scence and Technology College of Information and Control Engineering Liaoning Shenyang China

ISBN: (纸本)9798350301595

In today's society with energy crisis and serious environmental pollution, it is very important to use a clean and pollution-free power generation (PG) method. The use of wind solar hybrid PG system can solve the power consumption problem in areas without power, and improve people's energy structure today. Nowadays, there is still a problem of insufficient efficiency in Wind Solar Complementary PG systems. Therefore, this article focuses on the optimization design of Wind Solar Complementary PG system configuration based on grey prediction PID technology, which improves efficiency through grey prediction PID technology. This article tested through experiments that the PG efficiency of the Wind Solar Complementary PG system using grey prediction PID is the highest at 77% and the lowest at 70%, while the traditional Wind Solar Complementary PG system has the highest and lowest efficiency at 58% and 50%. It can be seen that the grey prediction PID technology has a good fit with the Wind Solar Complementary PG system. © 2023 IEEE.

关键词： Forecasting

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Dynamic Modeling Method for Turbofan Engines Based on Improved NARX Networks 50

Dynamic Modeling Method for Turbofan Engines Based on Improv...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Gu, Nannan Zhao, Zhihong He, Jinbao Huang, Jing Bao, Jianyu Wang, Yunlai School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Mechatronics Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665464543

This paper proposes a NARX network-based modeling method to improve the real-time performance of dynamic component-level engine models. This method combines the component-level model with the NARX network black-box modeling technique. It collects training data by solving the common working equation of the component-level model (N-R model) using the Newton-Raphson method and uses a multilayer feedforward neural network to approximate the nonlinear mapping from the input state space to the solution of the common working equation. This approach eliminates the need for iterative computations in solving the common working equation, thus enhancing the real-time performance of the component-level model. Using the N-R model as the benchmark, simulation results show that the NARX network model with embedded component-level modeling has a maximum dynamic error of 0.061% during ground state modeling and 0.36% across the full envelope;the average runtime for full envelope modeling is 0.046 seconds, which is only 4.3% of that of the benchmark model;for step inputs, the component residual during the dynamic process is only 0.02% of that of the benchmark model. These findings demonstrate that the engine model established by this method has high accuracy, good real-time performance, and robustness. © 2024 IEEE.

关键词： Newton-Raphson method

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Learning-based tracking control of AUV:Mixed policy improvement and game-based disturbance rejection

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CAAI Transactions on Intelligence technology 2025年第2期10卷 510-528页

作者： Jun Ye Hongbo Gao Manjiang Hu Yougang Bian Qingjia Cui Xiaohui Qin Rongjun Ding State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina School of Information Science and Technology and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefeiChina Nanyang Technological University SingaporeSingapore Wuxi Intelligent Control Research Institute of Hunan University Hunan UniversityWuxiChina

A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe *** combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP ***,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling ***,the control object interacts with the real environment and continuously gathers adequate sampled data in the *** comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy *** a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control ***,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,***,the convergence property of the proposed algorithm based on the value iteration method is ***,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicle game theory optimal control reinforcement learning

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