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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Statistical Learning Theory for control: A Finite-Sample Perspective

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IEEE control systems 2023年第6期43卷 67-97页

作者： Tsiamis, Anastasios Ziemann, Ingvar Matni, Nikolai Pappas, George J. ETH Zürich Department of Information Technology and Electrical Engineering Zürich8092 Switzerland KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typically under the purview of control theory. While these algorithms have already shown tremendous promise in certain applications [1], there are considerable challenges, in particular, with respect to guaranteeing safety and gauging fundamental limits of operation. Thus, as we integrate tools from machine learning into our systems, we also require an integrated theoretical understanding of how they operate in the presence of dynamic and system-theoretic phenomena. Over the past few years, intense efforts toward this goal - an integrated theoretical understanding of learning, dynamics, and control - have been made. While much work remains to be done, a relatively clear and complete picture has begun to emerge for (fully observed) linear dynamical systems. These systems already allow for reasoning about concrete failure modes, thus helping to indicate a path forward. Moreover, while simple at a glance, these systems can be challenging to analyze. Recently, a host of methods from learning theory and high-dimensional statistics, not typically in the control-theoretic toolbox, have been introduced to our community. This tutorial survey serves as an introduction to these results for learning in the context of unknown linear dynamical systems (see 'Summary'). We review the current state of the art and emphasize which tools are needed to arrive at these results. Our focus is on characterizing the sample efficiency and fundamental limits of learning algorithms. Along the way, we also delineate a number of open problems. More concretely, this article is structured as follows. We begin by revisiting recent advances in the finite-sample analysis of system identification. Next, we discuss how these finite-sample bounds can be used downstream to give guaranteed performa

关键词： Learning algorithms

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On the Segmentation of Sunflower Plants in UAV Photos 25

On the Segmentation of Sunflower Plants in UAV Photos

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25th International Conference on Digital Signal Processing and its Applications, DSPA 2023

作者： Poleshchenko, Dmitriy Mikhailov, Ilia Petrov, Vladislav National University of Science and Technology Misis Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (纸本)9798350337235

In precision farming, it is important to assess indicators such as seedling density, leaf area index (LAI), and to identify plant growth stages for yield prediction. Plant detection can provide a measure of crop density. Calculation of segmented pixel area in an image can be used to determine crop development and LAI. In this paper, an approach to solve the sunflower plant segmentation problem from photos produced by a UAV is considered. The problem is complicated by the limited size of the training sample. Unet and Feature Pyramid Network (FPN) are considered for segmentation. The result is that FPN is on average 1.05% more accurate than Unet and the average mIoU of the three classes is 1.26% higher. The effect of image brightness and contrast on accuracy in the case of different photo capture conditions is considered. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Development of a System for Automated control of Planting Density, Leaf Area Index and Crop Development Phases by UAV Photos 7

Development of a System for Automated Control of Planting De...

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7th International Conference on information, control, and Communication Technologies, ICCT 2023

作者： Poleshchenko, Dmitriy Petrov, Vladislav Mikhailov, Ilia National University of Science and Technology 'MISIS' Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (纸本)9798350340945

The paper describes the results of research into the possibility of creating a system for automated control of crop development indicators using UAV images and neural networks. Such architectures as YOLOv5, U-Net and FPN (Feature Pyramid Network) are considered. It is found out that YOLOv5m neural network is able to qualitatively detect plants on a sample of 363 images with an accuracy AP@5:.95 of 92.4 %. The detection efficiency can be improved both by adding original images to the training sample and by increasing it artificially. Plant detection will help to determine the density of crops. It is shown that the calculation of the area of segmented pixels in an image can be used to determine crop development and leaf area index. FPNs are used for segmentation and it is obtained that FPNs are on average 1.05% more accurate than Unet and the average mIoU across the three classes is 1.26% larger. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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University Scheduling System on the Bologna Form of Education 5

University Scheduling System on the Bologna Form of Educatio...

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5th International Conference on Problems of Cybernetics and Informatics, PCI 2023

作者： Ismibayli, Reshad Rzayeva, Sona University of Architecture and Construction Faculty of Mechanics and Information Technology Department of Information Technology and Systems Baku Azerbaijan Institute of Control Systems Laboratory of Recognition Identification and Methods of Optimal Solutions Baku Azerbaijan

ISBN: (纸本)9798350319064

The paper considers the features of the process of scheduling in higher educational institutions in the conditions of the Bologna system of education. The stages of scheduling and the requirements and limitations to them are described. The mathematical formulation of the problem is given.A model for scheduling classes in an interactive mode, built on the principle of a traffic light, has been developed. The possibilities of the developed software are described, which provide facilitation and acceleration of the user's work, excluding the possibility of errors in the compiled schedule. © 2023 IEEE.

关键词： Learning systems

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Development and Optimization of Simulation Model of Decision Aid System Implementation 4

Development and Optimization of Simulation Model of Decision...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Panfilova, Tatyana Gladkov, Alexey Tynchenko, Vadim Kukartseva, Oksana Kvesko, Mikhail Siberian State University of Science and Technology Department of Information Economic Systems Reshetnev Krasnoyarsk Russia Bauman Moscow State Technical University Artificial Intelligence Technology Scientific and Education Center Russia Siberian State University of Science and Technology Information-Control Systems Department Reshetnev Russia Siberian Federal University Department of Technological Machines and Equipment of Oil and Gas Complex Krasnoyarsk Russia Siberian State University of Science and Technology Department of Systems Analysis and Operations Research Reshetnev Krasnoyarsk Russia Siberian State University of Science and Technology Department of Information and Control Systems Reshetnev Krasnoyarsk Russia

ISBN: (纸本)9798350349900

The use of simulation modeling of production activities to improve the effectiveness of management decisions allows to evaluate the quality of the decision made on the model, as well as a means of training employees. The relevance of the research topic in the management of the project to create a decision support system (DAS) using simulation modeling is that in today's world more and more organizations face the need to make decisions based on the analysis of large amounts of data and information. For this purpose, it is necessary to use special tools and methods that will allow to process data quickly and accurately and make decisions based on the results obtained. © 2024 IEEE.

关键词： Digital elevation model

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A Contact-Aided Model for Soft Continuum Robots Moving in Unstructured Environment

A Contact-Aided Model for Soft Continuum Robots Moving in Un...

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2024 China Automation Congress, CAC 2024

作者： Jingyu, Zhang Qin, Fang Mingjian, Sun Rong, Xiong Wang, Yue Haojian, Lu School of Information Science and Engineering Harbin Institute of Technology Weihai China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Control Science and Engineering Harbin Institute of Technology Heilongjiang China

ISBN: (纸本)9798350368604

Due to their inherent flexibility and high human-computer interaction potential, soft continuum robots have been developed and popularized in industrial and medical scenarios. However, when moving in an unstructured environment, the contact with environment will change the configuration parameters of the robot, which brings problems to the control. Aiming at improving the control performance of soft continuum robots in an unstructured environment, a contact-aided model is proposed to estimate the varying configurations of the robot. Besides, a model-based controller is proposed to control the robot in an unstructured environment. To verify the performance of the proposed method, a small-scale soft continuum robot is designed and fabricated. Through the characterization experiment, the robot shows a large workspace by changing the contact position, and the kinematic model is verified. Through the trajectory tracking experiment, the robot showed good accuracy, with an average error of 1.2mm. Through an exploration experiment in the unstructured environment, the robot showed good control effect, with an average error of 1.9mm. This work enhances the application capability of the soft continuum robots in unstructured environments. © 2024 IEEE.

关键词： Microrobots

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Generic model of IoT Edge device for object monitoring via LoRaWAN

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IFAC-PapersOnLine 2024年第9期58卷 160-165页

作者： Peter Peniak Emília Bubeníková Peter Holečko Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Slovakia

This article deals with the generic model for object monitoring and tracking in an industrial environment and manufacturing. We focus on the design of generic IoT Edge device model that enables the integration of various LoRa sensors independently on vendor solutions for common use cases. Our model is based on the MQTT protocol, which is proven for sensor applications and gives needed independency on proprietary protocols. The model was implemented in Node-RED software and subsequently validated with sensors of different vendors to confirm the generic approach and base for independent object tracking solutions. The proposed Edge device model supports independent object tracking, such as object status monitoring, localization and triggered events, giving independent approach for tailored solutions and wide application portfolio based on combined different sensors and devices.

关键词： LoRaWAN GPS sensors monitoring tracking generic model MQTT NodeRED

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Robust Data-Driven Tube-Based Zonotopic Predictive control with Closed-Loop Guarantees 63

Robust Data-Driven Tube-Based Zonotopic Predictive Control w...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Farjadnia, Mahsa Fontan, Angela Alanwar, Amr Molinari, Marco Johansson, Karl Henrik Kth Royal Institute of Technology Department of Energy Technology Sweden Kth Royal Institute of Technology Division of Decision and Control Systems Sweden Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9798350316339

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations. © 2024 IEEE.

关键词： Predictive control systems

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