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An Approach to Formation control of UAVs Based on Applying Adapted Kohonen Neural Network

An Approach to Formation Control of UAVs Based on Applying A...

2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

作者： Khachumov, Mikhail Ailamazyan Program Systems Institute of Ras Intelligent Control Laboratory Pereslavl-Zalessky Russia Federal Research Center' Computer Science and Control' of Ras Moscow Russia

ISBN: (纸本)9798350336054

The paper gives a statement and considers the solution of an urgent scientific problem of formation control for a group of unmanned aerial vehicles (UAVs) operating in an unstable environment. To construct the reference trajectories and allocate UAVs to positions of a given structure, an original approach is proposed based on the adapted Kohonen neural network with a set of metrics, including Euclidean, Mahalanobis and Euclidean-Mahalanobis distances. To implement the movement of a UAV group in a complex environment, we apply the principles of intelligent-geometric control, which allows to combine flexible intelligent and precise geometric control methods within one concept. When moving along a given route, UAVs use a set of allowable control strategies. The developed modeling scheme is designed to take into account wind loads and possibly dangerous rapprochement of vehicles. To reflect the dynamics of UAVs, a special module is used that contains transfer functions integrated into a single stabilization system with an autopilot. The proposed approach has shown promise when simulating a series of formation control problems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optical flow-based vertical angular rate fault detection on UAVs

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IFAC-PapersOnLine 2024年第4期58卷 109-114页

作者： Gabor Jevuczo Peter Bauer Systems and Control Laboratory Institute for Computer Science and Control (HUN-REN SZTAKI) HUN-REN Budapest Hungary

This paper discusses optical flow-based vertical angular rate fault detection based-on real fight IMU data and camera image sequences. The fight test platform utilizes a gimbal stabilized downward looking camera that is why only the angular rate around the inertial vertical axis can be estimated. Improvements relative to the previous work of the authors are the application of forward-backward optical flow to filter outliers, the direct detection of angular rate faults without the need for Euler angles or GPS velocities and tuning and verification on real images and fight data. After presenting the optical flow equations and the method for image-based vertical angular rate estimation the fight test scenarios and video processing steps are introduced. Four different Artificial errors are added to the IMU measurements and the fault detection applies residual thresholding and up-down counters. Tuning is based on false alarm and missed detection rates and the application of receiver operating characteristics. Finally, the estimation results and possible further improvements are presented.

关键词： angular rate fault detection real fight video optical flow UAV

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Space-Filling Input Design for Nonlinear State-Space Identification ⁎

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IFAC-PapersOnLine 2024年第15期58卷 562-567页

作者： Máté Kiss Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal.

关键词： Experiment Design Nonlinear System Identification State-Space

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Finite Dimensional Koopman Form of Polynomial Nonlinear systems*

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IFAC-PapersOnLine 2023年第2期56卷 6423-6428页

作者： Lucian C. Iacob Maarten Schoukens Roland Tóth Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process using so-called observable functions. While there is an extensive theory on infinite dimensional representations in the operator sense, there are few constructive results on how to select the observables to realize them. When it comes to the possibility of finite Koopman representations, which are highly important from a practical point of view, there is no constructive theory. Hence, in practice, often a data-based method and ad-hoc choice of the observable functions is used. When truncating to a finite number of basis, there is also no clear indication of the introduced approximation error. In this paper, we propose a systematic method to compute the finite dimensional Koopman embedding of a specific class of polynomial nonlinear systems in continuous-time, such that the embedding can fully represent the dynamics of the nonlinear system without any approximation.

关键词： Nonlinear systems Koopman operator Linear embedding

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An Alternative control Structure for the Naphtha Distillation Plant Based on Multivariable Model Predictive control 5

An Alternative Control Structure for the Naphtha Distillatio...

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5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Shevlyagina, Svetlana Far Eastern Federal University Laboratory of Control System the Technological Processes Institute of Automation and Control Processes FEB RAS Department of Computer-Integrated Production Systems Vladivostok Russia

ISBN: (纸本)9798350309126

An alternative end product quality control system is proposed for an industrial plant involving a sequence of distillation columns using a multivariable model predictive control (MPC) approach. The latter was realized using the MATLAB toolbox. A controllability analysis using the steady-state controllability metrics is presented and discussed to make a comparison between the currently applied and alternative control systems. Next, obtained results are verified with a closed-loop performance under the set point change. The tests show the advantage of an alternative control structure. Conclusions are drawn about the feasibility of using the proposed approach to manage the quality of the end products. © 2023 IEEE.

关键词： controllability

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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On the reduction of Linear Parameter-Varying State-Space models

arXiv

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arXiv 2024年

作者： Olucha, E. Javier Terzin, Bogoljub Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

This paper presents an overview and comparative study of the state of the art in state-order reduction (SOR) and scheduling dimension reduction (SDR) for linear parameter-varying (LPV) state-space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR, the following methods are compared: linear time-invariant (LTI), LPV and LFR-based balanced reductions, moment matching (MM) and parameter-varying oblique projection. For SDR, the following methods are compared: principal component analysis (PCA), trajectory PCA, Kernel PCA and LTI balanced truncation, autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations, from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model. © 2024, CC BY.

关键词： Principal component analysis

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