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Chinese Automation Congress (CAC)

作者： Yichuan Zhu Na Yao Hu Fan National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China System simulation laboratory Beijing Aerospace Automatic Control Institute Beijing China The third representative office of air force equipment in Beijing area Beijing China

This article designs a sliding mode terminal guidance law for high-speed targets by modifying the approach law. Firstly, the relative motion equation of the chaser and escaper is established; Then adjust the aircraft based on the changes in the distance of the chaser and escaper and approach speed, design a self adaptive approach law, and derive the guidance law. At the same time, prove the stability of the guidance system by constructing a Lyapunov function; Finally, simulation verification was conducted. The simulation results showed that the designed guidance law can achieve the encounter between the chaser and the escaper. By comparing it with proportional guidance under different operating conditions, it was verified that the method has higher adaptability to different operating conditions, thus verifying the effectiveness of the guidance law designed in this paper and providing certain reference value for further research.

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Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background 10th

Real-Time Vehicle Detection Method Based on Aerial Image in ...

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10th China Conference on Command and control, C2 2022

作者： Wu, Chen Liang, Ruofei He, Shuangcheng Wang, Hongshuo Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Beijing Science and Technology Leike Electronic Information Technology Co. Ltd. Beijing China Beijing Institute of Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9789811960512

In the field of military and security, the vehicle target has high capture value because of its high mobility, large load and high threat. In recent years, with the development of UAV technology and the improvement performance, as well as the rapid development and wide application of deep convolutional neural network in the field of object detection, vehicle detection based on UAV has become an important task, which has application value and practical significance that cannot be ignored. Compared with the traditional ground monitoring system, the UAV has high mobility, the background of the aerial image is more complex, and the target scales vary widely. Therefore, based on deep convolutional neural network, this paper proposes a feature fusion method combining context, which enhances the feature extraction ability of small targets in complex background. Then, a multi-scale region proposal matching method based on location clustering is proposed to enhance the matching degree between target features and region proposal. Finally, this paper designs a lightweight backbone based on DenseNet [1], which effectively reduces the computational cost. Experimental results show that our model reached the accuracy of 86.7% on PASCAL VOC (only car and bus are selected) dataset, which has a 3.1% bonus compared with the baseline SSD [2]. Moreover, in order to show the effect on aerial images, an additional experiment is conducted on the UAV123 dataset, which gets a 5.7% bonus. The speed of our model has a 180 fps on the NVIDIA RTX 2080. © 2022, Chinese institute of Command and control.

关键词： Unmanned aerial vehicles (UAV)

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Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation

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Applied Mathematics and Nonlinear sciences 2024年第1期9卷

作者： Zhao, Demin Xiong, Wei Shi, Jinjing Science and Technology on Complex Electronic System Simulation Laboratory University of Aerospace Engineering Beijing101400 China Dfh Satellite CO. Ltd Beijing100080 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China

With the popularization and development of communication technology, the resource allocation problem of satellites has become a hot research topic. This study proposes a model for satellite resource allocation through the development of a beam mobilization system model and problem modeling. Then, on the basis of the linear stochastic gradient descent method, the classification accuracy of the algorithm is improved by adjusting the algorithm-solving method. Then its weight assignment method is improved to get the improved weighted linear stochastic gradient descent method. Using the optimized weighted linear stochastic gradient descent method to solve the satellite resource allocation problem from the perspective of the original problem, a model based on IWLSGD is designed and tested for performance. Through simulation experiments, the beam allocation service value of the satellite resource allocation model in this paper is 193, which is 4.32% and 3.21% higher than that of LSGD and WLSGD, respectively, and iterative convergence is faster, and its operation time and service value performs the best under different numbers of communication time slots. The system revenue, system access success rate, and system satisfaction under the interference environment between LEO and GEO are maintained at 15208, 0.8~1.0, and 85%, and keep around 7500, 1, and 75% under 5G base station interference. The satellite resource allocation model in this paper can effectively improve the utilization efficiency of communication resources and better adapt to dynamically changing interference scenarios. © 2024 Demin Zhao et al., published by Sciendo.

关键词： Resource allocation

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Cooperative Task Allocation of Multiple VA Vs Based on Greedy Algorithm 4

Cooperative Task Allocation of Multiple VA Vs Based on Greed...

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4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Chen, Yuheng C., Du J., Chen W., Yu Northwestern Poly Technical University Department of Computer Science Xi' an China North Automatic Control Technology Institute Department of Software System Development Taiyuan030000 China

ISBN: (纸本)9781665438902

Multiple VAVs can perform certain large-scale tasks by constructing VA V clusters in practical applications since its excellent performance such as strong operability and composability. However, it is difficult to determine the specific execution of each drone before performing the cluster task in a cluster composed of multiple drones. This paper studies the task assignment problem under the constraint of multi-VA V cooperative flight, and proposes a task planning algorithm based on the shortest flight path or time optimal under the condition of ensuring the cooperative flight between VAVs. The algorithm combines the directed graph in graph theory to represent the cooperative flight constraint relationship between tasks, and adopts the breadth-first search strategy to realize the traversal assignment of tasks. During the assignment process, according to the greedy algorithm, the tasks are locally optimally allocated to obtain the approximate optimal solution. Finally, a simulation experiment is performed on the constructed task scene to verify the effectiveness of the algorithm. © 2021 IEEE.

关键词： Drones

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Chongde Ren Jinchao Chen Chenglie Du Wenquan Yu School of Software Northwestern Polytechnical University Xi'an China Department of Computer Science Northwestern Polytechnical University Xi'an China Department of Land Unmanned System Research and Development North Automatic Control Technology Research Institute Taiyuan China

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.

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An Energy Efficient Dynamic Scheduling Algorithm for Reliability Constraints in Distributed Heterogeneous systems

An Energy Efficient Dynamic Scheduling Algorithm for Reliabi...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Yifan Liu Chenglie Du Jinchao Chen Xiaoyan Du Wenquan Yu School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Poly technical University Xi'an China Department of Land Unmanned System Research and Development North Automatic Control Technology Research Institute Taiyuan China

Scheduling algorithms in distributed heterogeneous systems have been the subject of extensive research. However, with the advancement of technology, addressing real-time, reliability, and energy consumption issues in dynamic environments has become increasingly critical. Existing studies mostly focus on static approaches, which are challenging to achieve scheduling objectives rapidly and adaptively. Therefore, we propose a novel dynamic energy-aware scheduling algorithm for deadlines and reliability constrained applications. Firstly, we introduce a prioritization method for multitasking applications to ensure more applications meet their deadlines. Subsequently, we present specific strategies for task allocation to achieve a better trade-off between energy consumption and reliability. Experimental results on randomly generated applications validate the superiority of our proposed method in terms of performance compared to other algorithms.

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A stabilizing reinforcement learning approach for sampled systems with partially unknown models

arXiv

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arXiv 2022年

作者： Beckenbach, Lukas Osinenko, Pavel Streif, Stefan The Laboratory for Automatic Control and System Dynamics Technische Universität Chemnitz Chemnitz09107 Germany The Digital Engineering Center Skolkovo Institute of Science and Technology Moscow121205 Russia

Reinforcement learning is commonly associated with training of reward-maximizing (or cost-minimizing) agents, in other words, controllers. It can be applied in model-free or model-based fashion, using a priori or online collected system data to train involved parametric architectures. In general, online reinforcement learning does not guarantee closed loop stability unless special measures are taken, for instance, through learning constraints or tailored training rules. Particularly promising are hybrids of reinforcement learning with "classical" control approaches. In this work, we suggest a method to guarantee practical stability of the system-controller closed loop in a purely online learning setting, i. e., without offline training. Moreover, we assume only partial knowledge of the system model. To achieve the claimed results, we employ techniques of classical adaptive control. The implementation of the overall control scheme is provided explicitly in a digital, sampled setting. That is, the controller receives the state of the system and computes the control action at discrete, specifically, equidistant moments in time. The method is tested in adaptive traction control and cruise control where it proved to significantly reduce the *** Codes 93D15 © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Hekai Yao Xuetao Zhang Gang Sun Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China College of Artificial Intelligence The Institute of Robotics and Automatic Information System (IRAIS) Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

关键词： Laser radar Accuracy Uncertainty Probabilistic logic Distortion Robustness Sensors Odometry Distortion measurement State estimation

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Abnormal Test Data Diagnosis and Prognosis Based on Least Squares Support Vector Machine

Abnormal Test Data Diagnosis and Prognosis Based on Least Sq...

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2020 IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2020

作者： Han, Huilian Cui, Zhaojing National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

To figure out the shortage of traditional fault diagnosis method, the paper carried out fault diagnosis and forecast by detecting abnormal data based on LS-SVM algorithm. SVM has the optimal solution and good generalization ability in small samples and can avoid falling into local minimum. The paper introduced the principle of SVM, made up the model of non-linear least squares support vector regression solve various breakdowns coexist of actual circumstance, then identified and prognosis abnormal data by using high- dimension feature vector space as the input of LS-SVM. Examples showed that abnormal data could be detected effectively. The results proved that the model of fault diagnosis and prediction effective and has higher rate of correct judgments. © 2020 IEEE.

关键词： Data mining

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Dynamic Stiffness Analysis and Experimental Verification of Axial Magnetic Bearing Based on Air Gap Flux Variation in Magnetically Suspended Molecular Pump

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Chinese Journal of Mechanical Engineering 2020年第4期33卷 139-149页

作者： Jinji Sun Wanting Wei Jiqiang Tang Chun‑E Wang Key Laboratory of Fundamental Science for National Defense Novel Inertial Instrument and Navigation System TechnologySchool of Instrument Science and Opto‑Electronics EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China Beijing Institute of Automatic Control Equipment Beijing 100074China

Current and displacement stiffness are important parameters of axial magnetic bearing(AMB)and are usually considered as constants for the control ***,in actual dynamic work situations,time-varying force leads to time-varying currents and air gap with a specific frequency,which makes the stiffness of appear decrease and even worsens control performance for the whole *** this paper,an AMB dynamic stiffness model considering the flux variation across the air gap due to frequency is established to obtain the accurate dynamic *** dynamic stiffness characteristics are analyzed by means of the dynamic equivalent magnetic circuit *** analytical results show that the amplitude of current and displacement stiffness decreases with frequency ***,compared with the stiffness model without considering the variation of flux density across the air gap,the improved dynamic stiffness results are closer to the actual *** the dynamic stiffness measurement method of AMB,experiments of AMB in magnetically suspended molecular pump(MSMP)are carried out and the experimental results are consistent with theoretical analysis *** paper proposes the dynamic stiffness model of axial magnetic bearing considering the variation of flux density across the air gap,which improves the accuracy of the AMB stiffness analysis.

关键词： Axial magnetic bearing(AMB) Dynamic stiffness Magnetically suspended molecular pump(MSMP)

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