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第41届中国控制会议

作者： Guangjun Wang Yangzheng Zhang Yi Zhao Zhi Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the non-contact detection scene of GPR,it is necessary to adjust the repetition frequency of transmitting pulse waveform flexibly,and a GPR pulse signal design based on FPGA is *** scheme uses FPGA to control the generation of adjustable pulse signals,which are amplified by high-frequency power amplifier circuit,and the signal repetition frequency can be adjusted by *** theory of transmitting pulse waveform design is analyzed,the FPGA logic circuit is designed to generate pulse signal,and the high frequency power amplifier circuit is designed to solve the problem of low power of FPGA *** effectiveness of the scheme is verified by *** results show that the pulse signal generated by the scheme has stable frequency amplitude and the output power meets the requirements of non-contact *** with the traditional pulse signal generation device using switching devices,the structure is simple and the pulse signal can be set flexibly.

关键词： Ground Penetrating Radar FPGA Pulse Signal Power Amplifier

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Tracking and Mapping Strategy for Indoor UAV Based on Entropy Theory:An ORB-SLAM3 Extension 41

Tracking and Mapping Strategy for Indoor UAV Based on Entrop...

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第41届中国控制会议

作者： Jian Xu Haoyu Qi Min Xu Yunge Zang Zhen Li Xi Zhang Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For indoor application,which is an entirely GPS-denied environment,visual simultaneous localization and mapping(SLAM) facilitates the real autonomy of unmanned aerial vehicle(UAV) but raises the challenging requirement on accurate localization with low computational *** address this difficulty,a stereo-camera based SLAM system is proposed by applying Entropy theory to *** an extension to ORB-SLAM3,the additional entropy decision module and map processor are specifically *** decision module can improve computing efficiency by deciding whether keyframes or extra optimization should be ***,the map processor is targeted at loading and maintaining the prior map whenever *** results in indoor laboratory environment show that the developed system can achieve the superior localization accuracy in more efficient computation manner with smaller size of mapping compared with ***,the map can be effectively expanded and corrected even when prior information is invalid,greatly increasing the robustness of SLAM system.

关键词： UAV stereo-camera ORB-SLAM3 entropy theory prior map

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Distributed Solver for Discrete-Time Lyapunov Equations over Dynamic Networks with Linear Convergence Rate

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IEEE Transactions on Cybernetics 2022年第2期52卷 937-946页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

The problem of solving discrete-Time Lyapunov equations (DTLEs) is investigated over multiagent network systems, where each agent has access to its local information and communicates with its neighbors. To obtain a solution to DTLE, a distributed algorithm with uncoordinated constant step sizes is proposed over time-varying topologies. The convergence properties and the range of constant step sizes of the proposed algorithm are analyzed. Moreover, a linear convergence rate is proved and the convergence performances over dynamic networks are verified by numerical simulations. © 2013 IEEE.

关键词： Convex optimization

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Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance 20

Relative Localization for UAV-UGV Based on Correlation of Be...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Guo, Jia Hu, Kang Liu, Jinting State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough. © 2023 IEEE.

关键词： Antennas

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Target Recognition Inference Based on Knowledge Graph and Graph Neural Network

Target Recognition Inference Based on Knowledge Graph and Gr...

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2024 China Automation Congress, CAC 2024

作者： Li, Wei Xu, Hongfeng Li, Yuan Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350368604

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Knowledge graph

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning 42

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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42nd Chinese control Conference, CCC 2023

作者： Khan, Malak Abid Ali Ma, Hongbin Jin, Ying Ma, Jingxiang Rehman, Zia Ur Rahman, Mizanur School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deforestation

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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Unified Addressing Algorithm for Tasks in Cloud control systems 41

Unified Addressing Algorithm for Tasks in Cloud Control Syst...

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第41届中国控制会议

作者： Guan Wang Yufeng Zhan Yuanqing Xia Liping Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information Science and Engineering University of ZaoZhuang

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of cloud computing and control theory,a new paradigm of networked control systems named cloud control systems has been proposed to satisfy the requirements of large-scale and complex *** is critical to manage large-scale tasks with personalized needs on the large number of heterogeneous ***,current TCP/IPbased addressing approach does not consider the runtime constraints of tasks,resulting highly concurrent service search,and communication between tasks will become the system *** this paper,we propose a task property encoding method which takes multi-dimensional properties into *** on the proposed encoding method,we design a uniform addressing method for tasks in the ***,we experimentally demonstrate the effectiveness of task property encoding,and the unified addressing method for tasks is more effective than other methods in the case of high task concurrency.

关键词： Cloud control systems address encoding cloud-edge-device cooperation

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Lane-Change Intention Prediction of Surrounding Vehicles Using BiLSTM-CRF Models with Rule Embedding

Lane-Change Intention Prediction of Surrounding Vehicles Usi...

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2022 Chinese Automation Congress, CAC 2022

作者： Wang, Kai Hou, Jie Zeng, Xianlin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9781665465335

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers' distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM. © 2022 IEEE.

关键词： Embeddings

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