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Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1557-1565页

作者： Jin-Xi Zhang Kai-Di Xu Qing-Guo Wang IEEE the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China institute of artificial intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Laboratory of artificial intelligence and Multi-Modal Data Processing Beijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this ***, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

关键词： Nonlinear systems output constraints prescribed performance reference tracking time delays

Global output feedback control of feedforward nonlinear time-delay systems with unknown growth rate and unknown measurement sensitivity

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Control Theory and Technology 2024年第1期22卷 122-134页

作者： Weiyong Yu Keao Gao Hongbing Zhou Qiang Liu Academy of artificial intelligence Beijing Institute of Petrochemical TechnologyBeijing102617China School of Mathematics and Statistics Nanyang Normal UniversityNanyang473061HenanChina

This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.

关键词： Adaptive control Dynamic gain controllers Feedforward time-delay systems Measurement sensitivity Output feedback

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

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Computers, Materials & Continua 2024年第6期79卷 4621-4639页

作者： Xuejie Wang Jianxun Zhang Ye Tao Xiaoli Yuan Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Liangjiang institute of artificial intelligence Chongqing University of TechnologyChongqing400054China

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture ***-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture *** capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among *** paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced *** contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the *** proposed loss function ensures effective retention of key information from the source images of both *** comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture *** the qualitative results,we can see the advantages of fusion details and local *** the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

关键词： Deep learning feature enhancement computer vision

Dynamic Scene Graph Generation of Point Clouds with Structural Representation Learning

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Tsinghua Science and Technology 2024年第1期29卷 232-243页

作者： Chao Qi Jianqin Yin Zhicheng Zhang Jin Tang School of artificial intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China Standard and Metrology Research institute China Academy of Railway Sciences Corporation LimitedBeijing 100081China

Scene graphs of point clouds help to understand object-level relationships in the 3D *** graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud *** point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual *** address these problems,we explore a method to convert the point clouds into structured data and generate graphs without given ***,we cluster points with similar augmented features into groups and establish their relationships,resulting in an initial structural representation of the point ***,we propose a Dynamic Graph Generation Network(DGGN)to judge the semantic labels of targets of different *** dynamically splits and merges point groups,resulting in a scene graph with high *** show that our methods outperform other baseline *** output reliable graphs describing the object-level relationships without additional manual labeled data.

关键词： scene graph generation structural representation point cloud

A multi-stream network for retrosynthesis prediction

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Frontiers of Computer Science 2024年第2期18卷 231-233页

作者： Qiang ZHANG Juan LIU Wen ZHANG Feng YANG Zhihui YANG Xiaolei ZHANG institute of artificial intelligence School of Computer ScienceWuhan UniversityWuhan 430072China College of Informatics Huazhong Agricultural UniversityWuhan 430072China

1 Introduction Retrosynthesis aims to predict a set of reactants for producing given molecules[1],which plays a significant part in the biochemistry field,such as molecular pathway design and drug *** existing methods perform better in solving this problem,most of these methods[2,3]only describe the molecules from one kind of perspective,such as they use Simplified Molecular Input Line Entry System(SMILES)strings[4]to represent the atomic components and the relationship between atoms of molecules or leverage Extended Connectivity Fingerprints(ECFPs)represent the information of the molecular sub-structure composition via binary vectors[5].

关键词： synthesis reactant hough

Prescribed-Time Fully Distributed Nash Equilibrium Seeking Strategy in Networked Games

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 261-263页

作者： Cheng Qian Lei Ding the institute of Advanced Technology for Carbon Neutrality College of Automation&the College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China IEEE

Dear Editor, This letter is concerned with prescribed-time fully distributed Nash equilibrium seeking for networked games under directed graphs. An adaptive algorithm is proposed to ensure the convergence of all players to the Nash equilibrium without requiring any knowledge of global parameters. Moreover, it is theoretically proved that the convergence time of the proposed seeking strategy can be predefined based on practical requirements. Finally, a numerical example is presented to validate the effectiveness of the proposed method.

关键词： parameters seeking prescribed

Autonomous multi-drone racing method based on deep reinforcement learning

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Science China(Information Sciences) 2024年第8期67卷 35-48页

作者： Yu KANG Jian DI Ming LI Yunbo ZHAO Yuhui WANG Department of Automation University of Science and Technology of China institute of Advanced Technology University of Science and Technology of China institute of artificial intelligence Hefei Comprehensive National Science Center College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has a low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments.

关键词： autonomous multi-drone racing deep reinforcement learning Markov game racing drone sim-to-real

Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of artificial intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

UAV image target localization method based on outlier filter and frame buffer

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Chinese Journal of Aeronautics 2024年第7期37卷 375-390页

作者： Yang WANG Hongguang LI Xinjun LI Zhipeng WANG Baochang ZHANG institute of Unmanned System Beihang UniversityBeijing 100191China School of Electronics and Information Engineering Beihang UniversityBeijing 100191China institute of artificial intelligence Beihang UniversityBeijing 100191China

With rapid development of UAV technology,research on UAV image analysis has gained *** the existing techniques of UAV target localization often rely on additional equipment,a method of UAV target localization based on depth estimation has been ***,the unique perspective of UAVs poses challenges such as the significant field of view variations and the presence of dynamic objects in the *** a result,the existing methods of depth estimation and scale recovery cannot be directly applied to UAV ***,there is a scarcity of depth estimation datasets tailored for UAV perspectives,which makes supervised algorithms *** address these issues,an outlier filter is introduced to enhance the applicability of depth estimation networks to target localization.A frame buffer method is proposed to achieve more accurate scale recovery,so as to handle complex scene textures in UAV *** proposed method demonstrates a 14.29%improvement over the *** with the average recovery results from UAV perspectives,the difference is only 0.88%,approaching the performance of scale recovery using ground truth ***,to overcome the limited availability of traditional UAV depth datasets,a method for generating depth labels from video sequences is *** to state-of-the-art methods,the proposed approach achieves higher accuracy in depth estimation and stands for the first attempt at target localization using image *** algorithm and dataset are available at https://***/uav-tan/uav-object-localization.

关键词： Object localization Deep learning Depth estimate Scale recovery Unmanned Aerial Vehicle(UAV)