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2023 China Automation Congress, CAC 2023

作者： Gong, Hua Hu, Yiwen Tang, Shuwei Ma, Yuanjing Xu, Lei College of Science Shengyang Ligong University Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry Shenyang110159 China College of Science Shengyang Ligong University Shenyang110159 China National Key Laboratory of Electromagnetic Space Security Tianjin300308 China

ISBN: (纸本)9798350303759

In comparison to visible light images, infrared images are less susceptible to factors like illumination and weather conditions, making them more suitable for applications in security, military, and various other fields. The utilization of infrared image generation techniques enables the rapid and cost-effective acquisition of a considerable number of simulated infrared images. Considering the characteristics of low contrast, low texture, weak information, and high noise in infrared images, this paper proposes a comprehensive strategy that includes a fusion of adaptive segmented linear transformation for image enhancement and arithmetic mean filtering for image denoising, in order to preprocessing infrared image data. Furthermore, an algorithm named Data Preprocessing-Arbitrary Style Transfer via Multi Adaptation Network (DP-MAST) is provided, based on a multi-adaptation network, to achieve the generation of multi-time infrared image data from a single time period to any multi-time periods. The experimental results show that compared to other image generation techniques, the similarity between the infrared images generated by DP-MAST and the real images has reached 91.4%. © 2023 IEEE.

关键词： Image enhancement

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Impact of Position Perturbation on Distributed Collaborative Beamforming Using UAV Swarm

Impact of Position Perturbation on Distributed Collaborative...

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IEEE International Conference on Communications Workshops, ICC

作者： Wenbo Guo Jiaxin Du Mu Yan Hongzhi Zhao Shihai Shao National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

To improve the capability for communication, radar, and other wireless applications, the combination of distributed collaborative beamforming (DCBF) and virtual antenna array (VAA) based on unmanned aerial vehicle (UAV) swarm is a novel solution. DCBF using UAV swarm requires aligning the relative phases of each UAV in the VAA. However, the phase offset caused by position perturbation between the UAVs always exists in the practical system, which result in the deterioration of the VAA peak power and 3-dB beamwidth. Thus, inspired by such problem, this paper explicitly analyzes the impact of position perturbation on DCBF using UAV swarm, where the expression of average far-field beampattern and the approximate expression of 3-dB beamwidth in the presence of position perturbation are derived. Theoretical analyses and simulation results show that the average far-field beampattern deteriorates, the VAA peak power decreases, and the 3-dB beamwidth degrades as the position perturbation intensifies. Moreover, according to the simulation results, as the UAV swarm scale increases, the deterioration of peak power is aggravated, while the deterioration of 3-dB beamwidth is alleviated.

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Event-based Neuro-Adaptive Containment control for Uncertain Networked Agent Systems With External Disturbances 43

Event-based Neuro-Adaptive Containment Control for Uncertain...

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43rd Chinese control Conference, CCC 2024

作者： You, Long Lin, Boxian Yue, Jiangfeng Li, Weihao Shi, Mengji Qin, Kaiyu University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China National Laboratory on Adaptive Optics Chengdu610209 China

ISBN: (纸本)9789887581581

In this work, the robust containment control problem of uncertain networked agent systems is addressed. The dynamics of both the followers and the leaders involve uncertain nonlinear components. Theoretically, a neural network-based adaptive control scheme with the event-triggered mechanism is designed for the follower agents to approach the convex hull which is composed of the leader agents. Furthermore, a unified event-triggered mechanism is designed to ensure that the neural networks synchronize with the reduced update frequency of the robust controller, while ensuring that both are updated solely when the system error state reaches a predetermined triggering condition. The designed event-triggered containment control scheme minimizes unnecessary communication and computation resulting from periodic updates, thereby enhancing the efficiency of communication and computational resource utilization. Then, by utilizing the Lyapunov stability theorem, it is proven that all signals of the closed-loop system are globally uniformly ultimately bounded, and Zeno behavior is strictly excluded. Finally, the effectiveness of the proposed containment control scheme is verified through numerical simulation results. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Adaptive control systems

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Adaptive Containment control for Uncertain Networked Agent Systems with Second-Order Dynamics 13

Adaptive Containment Control for Uncertain Networked Agent S...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： You, Long Lin, Boxian Yue, Jiangfeng Shi, Mengji Qin, Kaiyu Ren, Zili School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China National Laboratory on Adaptive Optics Chengdu611731 China

ISBN: (纸本)9798350361674

This article proposes a neural network-based adaptive distributed containment controller for second-order integral multi-agent systems with nonlinear dynamics and external disturbances. Firstly, the proposed controller effectively suppresses the effects of nonlinear dynamics and external disturbances while achieving containment control. Secondly, for the adaptive update law of neural networks, unlike existing studies, we simultaneously consider the influence of position error and velocity error on the convergence speed of the weight matrix. Further, two positive definite matrices are constructed to prove the stability of the system based on Lyapunov theory, and the upper bound for the containment errors is provided. Finally, the effectiveness of the proposed method is verified through numerical simulation experiments. © 2024 IEEE.

关键词： Adaptive control systems

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Self-Noise Based Physical-Layer Secure Communication: Transceiver Design and Performance Analysis

Self-Noise Based Physical-Layer Secure Communication: Transc...

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IEEE Conference on Vehicular technology (VTC)

作者： Lang Lin Changqing Song Hongzhi Zhao Shihai Shao Youxi Tang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In single-input single-output (SISO) systems, transmitting the expected signals with artificially generated noise can effectively enhance the physical layer security (PLS). However, the traditional noise suppression at the intended receivers will increase algorithm complexity, particularly over multi-path fading channels, which poses challenges for some massive Internet-of-Things (IoT) networks that take advantage of low-cost and resource-constrained nodes. In this paper, we design a novel self-noise (SN) waveform for transmission security. The transmitter stacks the signal segments periodically, and the additive SN in the current signal period derives from the previous signal periods. Based on this, SN can be completely suppressed at intended receivers by differential operations between two adjacent signal periods. Both analysis and simulation results confirm that the proposed scheme can significantly reduce the complexity of noise suppression and its performance is not affected by multi-path channel environments. Besides, the proposed scheme can effectively worsen the signal-to-noise ratio (SNR) of eavesdroppers while having negligible impacts on intended receivers.

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A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1136-1148页

作者： Xiangjun Zeng Ming Yang Chen Feng Yaohua Tang Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061Chinaand X.Zeng is also with the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China Rundian Energy Science and Technology Co. Ltd.Zhengzhou 450046China

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating *** this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is *** CPEM can estimate the conditional probability density function(PDF)of the target variable given changing *** generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear *** using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly ***,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more *** effectiveness of the proposed method is tested by using the actual data of WTs with known *** results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

关键词： Anomaly detection combined probability estimation generalization hypothesis testing(HT)

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Noncooperative Target Finite-Time Surrounding control of spacecraft Formation

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space(science & technology) 2024年第1期4卷 260-270页

作者： Lifan Zhou Yanning Guo Yu Zhang Panxing Huang Pengyu Wang School of Astronautics Harbin Institute of TechnologyHarbin 150001China Beijing Institute of Control Engineering Beijing 100190China National Key Laboratory of Science and Technology on Space Intelligent Control Beijing 100190China Korea Advanced Institute of Science and Technology Daejeon 34141Republic of Korea

This paper proposes a novel distributed control method for surrounding a noncooperative target that has maneuverability by spacecraft formation.A relative orbit error dynamic model between the target and the formation is established dependent on a reference spacecraft under the 2-body *** estimate and compensate for the target’s control input rapidly,a novel finite-time extended state observer is *** is stable in the sense of fast finite-time uniformly ultimately bounded stability.A fast terminal sliding mode controller is proposed for finite-time convergence of the *** examples are implemented to show the effectiveness of proposed algorithm.

关键词： finite time surrounding control distributed control method relative orbit error dynamic model reference spacecraft spacecraft formationa estimate compensate spacecraft formation noncooperative target

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A new approach of initial localization for planetary lander with the Bag of Visual Words model

A new approach of initial localization for planetary lander ...

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2024 International Conference on Remote Sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Feng, Yaxuan Xu, Xiong Xie, Huan Feng, Yongjiu Ye, Zhen Wang, Chao Tong, Xiaohua College of Surveying and Geo-informatics Tongji University Shanghai China Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration Shanghai China Shanghai Institute of Intelligent Science and Technology Shanghai China

ISBN: (纸本)9781510685826

Precise localization of a planetary lander during descent phase is crucial for ensuring a safe landing. Terrain Relative Navigation (TRN) is a critical technology for achieving precise localization in hazardous terrains. Initial localization which aligns the descent image from lander camera with the reference map, is essential for TRN to generate precise localization results. This paper introduces a novel initial localization method utilizing the Bag of Visual Words (BoVW) model to pinpoint the descent image on the global map. This method enhances localization efficiency by narrowing the search range for image-map matching. This approach was validated with both synthesized and actual descent image data from the Mars 2020 mission. © 2024 SPIE.

关键词： Planetary landers

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Time-Frequency Robust Cooperative Jamming Cancellation for Physical Security in Tactical Networks

Time-Frequency Robust Cooperative Jamming Cancellation for P...

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IEEE International Conference on Communications Workshops, ICC

作者： Wenbo Guo Mingyue Jin Yi Fang Yimin He Hongzhi Zhao Shihai Shao National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In tactical networks, cooperative jamming has the potential to limit enemy use of the electromagnetic spectrum from the physical layer and shield its own secret signals from eavesdropping. The unauthorized equipment is confused by the cooperative jamming actively released by our forces, but inevitably, the cooperative jamming will also propagate to the authorized receiver. In order to receive the desired signal correctly for the authorized receiver, the cooperative jamming should be reconstructed and cancelled. In this paper, we formulate a time-frequency robust cooperative jamming cancellation (CJC) architecture to reconstruct and cancel cooperative jamming received by authorized receivers, considering the existence of multi-path delay, mismatch of oscillator output frequency, and Doppler frequency shift in the transmission process. Simulation results show that the proposed architecture can improve the CJC performance, has a strong tolerance for incomplete time-frequency alignment, and can obtain better performance for large time delay and frequency offset by increasing the corresponding expansion order in the model.

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AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection

AgilePilot: DRL-Based Drone Agent for Real-Time Motion Plann...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Roohan Ahmed Khan Valerii Serpiva Demetros Aschalew Aleksey Fedoseev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Autonomous drone navigation in dynamic environments remains a critical challenge, especially when dealing with unpredictable scenarios including fast-moving objects with rapidly changing goal positions. While traditional planners and classical optimization methods have been extensively used to address this dynamic problem, they often face real-time, unpredictable changes that ultimately lead to suboptimal performance in terms of adaptiveness and real-time decision-making. In this work, we propose a novel motion planner, AgilePilot, based on deep reinforcement learning (DRL) that is trained in dynamic conditions, coupled with real-time computer vision (CV) for object detections during flight. The training-to-deployment framework bridges the Sim2Real gap, leveraging sophisticated reward structures that promote both safety and agility depending upon environmental conditions. The system can rapidly adapt to changing environments while achieving a maximum allowable speed of $3.0 ~\mathrm{m} / \mathrm{s}$ in real-world scenarios. In comparison, our approach outperforms classical algorithms such as the Artificial Potential Field (APF)-based motion planner by 3 times, both in performance and tracking accuracy of dynamic targets by using velocity predictions while exhibiting 90% a success rate in 75 conducted experiments. This work highlights the effectiveness of DRL in tackling real-time dynamic navigation challenges, offering intelligent safety and agility.

关键词： Computer vision Heuristic algorithms Dynamics Object detection Deep reinforcement learning Real-time systems Aircraft navigation Safety Planning Drones

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