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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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Knowledge and data jointly driven aeroengine gas path performance assessment method

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Chinese Journal of Aeronautics 2024年第5期37卷 533-557页

作者： Zhe WANG Xuyun FU Rui ZHANG Zhengfeng BAI Xiangzhao XIA Wei JIANG Department of Mechanical Engineering Harbin Institute of TechnologyWeihai 264209China Weihai Key Laboratory of Intelligent Operation and Maintenance Harbin Institute of TechnologyWeihai 264209China Control System Design Laboratory AECC ShenYang Engine Research InstituteShenyang 110015China

Aeroengines,as the sole power source for aircraft,play a vital role in ensuring flight *** gas path,which represents the fundamental pathway for airflow within an aeroengine,directly impacts the aeroengine's performance,fuel efficiency,and ***,timely and accurate evaluation of gas path performance is of paramount *** paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method,combining Fingerprint and gas path parameter deviation ***,Fingerprint is used to correct gas path parameter deviation values,eliminating parameter shifts caused by non-component performance ***,coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding ***,an Ensemble Empirical Mode Decomposition and Non-Local Means(EEMD-NLM)filtering method is designed to“clean”data noise,completing the preprocessing for gas path parameter deviation ***,based on the characteristics of gas path parameter deviation values,a Dynamic Temporary Blended Network(DTBN)model is built to extract its temporal features,cascaded with Multi-Layer Perceptron(MLP),and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder(DTB-AutoEncoder).Eventually,by training this improved autoencoder,the aeroengine gas path multi-component performance assessment model is formed,which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values,thereby achieving the performance assessment of engine gas path *** practical application cases,the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.

关键词： Performance assessment Aeroengine Fingerprint Gas path parameter deviation values Jointly drive

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Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

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Energy Informatics 2024年第1期7卷 88页

作者： Zhang, Jie Lu, Yuanhong Huang, Libin Guo, Haiping Tu, Liang State Key Laboratory of HVDC China Southern Power Grid Guangzhou510000 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou510000 China CSG Key Laboratory for Power System Simulation China Southern Power Grid Guangzhou510000 China

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances. © The Author(s) 2024.

关键词： Particle swarm optimization (PSO)

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Status and Application of Artificial Intelligence-Based Cooperative Positioning and Navigation Technology for UAV Clusters 1

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Sheng, Danting Cheng, Jin Li, Fengyi Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Intelligent Science and Technology Academy Limited of CASIC Beijing100041 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

Aiming at the UAV cluster cooperative navigation, this paper tries to summarize the development experience and existing results on two issues, namely, the application of filtering and artificial intelligence in UAV cluster cooperative navigation. The application of filtering in UAV cluster cooperative navigation briefly describes the application of Kalman filtering and extended Kalman filtering, traceless Kalman filtering and other nonlinear filters, intelligent optimization algorithms combined with particle filtering in cooperative navigation. Artificial Intelligence Methods in Cooperative Navigation of UAV Clusters describes the application of deep feed-forward networks, brain-like information fusion methods based on integrated learning, multivariate interpolation of radial-based neural networks and Kalman filtering combined in the field of cooperative navigation. The results of existing applications of filtering and artificial intelligence in UAV cluster cooperative navigation are analyzed and summarized, and the future development of the field is outlooked. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Kalman filters

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Dictionary-Labeled A∗: Optimal Path-and-Posture Planning for Mobile Robots with Turning Radius Constraints

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IEEE Robotics and Automation Letters 2024年第12期9卷 10740-10747页

作者： Yang, Chen Lin, Zhiyun Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China

In this study, we introduce an algorithm called Dictionary-Labeled A∗ (DL-A∗), which addresses the critical problem of path-and-posture planning for mobile robots with turning radius constraints. DL-A∗ utilizes a dictionary to store and process multi-element information within an enhanced topological map. The algorithm categorizes the edges in the map into forward driving and reverse driving states, and constructs sets of successive states that adhere to road segment constraints through map pre-processing. Subsequently, the A∗ algorithm is employed to find an optimal path to reach the endpoint while satisfying the end posture constraint, with the objective of minimizing the total travelling cost. Simulation results demonstrate that, in comparison to other popular algorithms, our algorithm offers the capability to adapt the robot's forward and reverse driving postures to minimize total cost, and exhibits exceptional searching efficiency. © 2016 IEEE.

关键词： Robot programming

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Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 463-470页

作者：朱江辉叶航航姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System Ministry of EducationShanghai 200240China Huawei Technologies Co. Ltd.Shanghai 201206China

Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization *** on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter *** paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic *** show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm *** with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.

关键词： traveling salesman problem(TSP) self-organizing mapping(SOM) combinatorial optimization neu-ral network

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Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs 24th

Compression Format and Systolic Array Structure Co-design f...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Cao, Yongxiang Jiang, Jixiang Zhao, Guocheng Wang, Wei Jiang, Hongxu Song, Yanfei School of Computer Science and Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819615445

The systolic array (SA) architecture is widely used in accelerator/AI chip design due to its excellent matrix multiplication acceleration effect. However, the mismatch between the traditional sparse matrix compression format and the SA architecture results in poor SA performance in computing sparse matrices. To solve the problem, we propose a method for co-designing the sparse compression format and SA architecture. This work proposes the Vector Group Compressed Coordinate (VGCC) data stream compression format and designs a sparse SA architecture based on hardware encoding and decoding. VGCC can handle data streams of any size without increasing the index bit width. The VGCC compression size is significantly smaller than general sparse compression formats such as COO/CSR. In a sparse matrix with a sparsity of 90%, it saves 67.13% of storage space compared to the state-of-the-art ECOO data stream compression format and achieves an average acceleration of 1.54 times. We also propose a VGCC-friendly SA architecture. Our SA architecture engine reduces logic consumption by 51.6% compared to Sparse TPU while achieving a 3.23x speedup. To mitigate the issue of high no-load rates in systolic arrays, we introduce a Data Transmission Priority (DTP) data matching algorithm. With sparsity levels ranging from 50% to 90%, the algorithm’s calculation speed is enhanced by 1.124x to 1.991x. We implemented a sparse DNN accelerator on an FPGA Zynq UltraScale+MPSoC ZCU102, utilizing the VGCC compression format proposed in this study for compressing input data. When compared to the S2 Engine, the accelerator achieved a 3.37x speed improvement in matrix multiplication with 75% sparsity, significantly enhancing the speed of sparse matrix multiplication. The accelerator achieved an average throughput of 1079.33GOP/s when inferencing DNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Systolic arrays

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HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

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Group Hybrid Coordination control of Multi-Agent systems With Time-Delays and Additive Noises

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 737-748页

作者： Chuanjian Li Xiaofeng Zong School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China IEEE

A new kind of group coordination control problemgroup hybrid coordination control is investigated in this *** group hybrid coordination control means that in a whole multi-agent system(MAS)that consists of two subgroups with communications between them,agents in the two subgroups achieve consensus and containment,*** MASs with both time-delays and additive noises,two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case,*** developing a new analysis idea,some sufficient conditions and necessary conditions related to the communication intensity betw een the two subgroups are obtained for the following two types of group hybrid coordination behavior:1)Agents in one subgroup and in another subgroup achieve weak consensus and containment,respectively;2)Agents in one subgroup and in another subgroup achieve strong consensus and containment,*** is revealed that the decay of the communication impact betw een the two subgroups is necessary for the consensus-oriented ***,the validity of the group control results is verified by several simulation examples.

关键词： Additive noises consensus control containment control group hybrid coordination control multi-agent systems(MASs) time-delays

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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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