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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wu, Dongjun Yi, Bowen Manchester, Ian R. Lund University Department of Automatic Control Box 118 LundSE-221 00 Sweden Polytechnique Montreal Department of Electrical Engineering & Gerad QCH3T 1J4 Canada The University of Sydney Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering Australia

ISBN: (纸本)9798350316339

In this paper, we extend the control contraction metrics (CCM) approach, which was originally proposed for the universal tracking control of nonlinear systems, to those that evolves on submanifolds. We demonstrate that the search for CCM on submanifolds can be reformulated as convex conditions. In particular, since Lie groups can be viewed as submanifolds in Euclidean space, the results are directly applicable to this setting. © 2024 IEEE.

关键词： Nonlinear simulations

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Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

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Energy Reports 2024年 11卷 4356-4378页

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Krakow Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system's performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2024 The Authors

关键词： MATLAB

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Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm

Hardware architecture for high throughput event visual data ...

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Euromicro Symposium on Digital System Design

作者： Marcin Kowalczyk Tomasz Kryjak Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Neuromorphic vision is a rapidly growing field with numerous applications in the perception systems of autonomous vehicles. Unfortunately, due to the sensors working principle, there is a significant amount of noise in the event stream. In this paper we present a novel algorithm based on an IIR filter matrix for filtering this type of noise and a hardware architecture that allows its acceleration using an SoC FPGA. Our method has a very good filtering efficiency for uncorrelated noise - over 99% of noisy events are removed. It has been tested for several event data sets with added random noise. We designed the hardware architecture in such a way as to reduce the utilisation of the FPGA's internal BRAM resources. This enabled a very low latency and a throughput of up to 385.8 MEPS million events per second. The proposed hardware architecture was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the Mercury+ XU9 module with the Mercury+ ST1 base board.

关键词： Visualization Filtering Computer architecture IIR filters Vision sensors Throughput Hardware

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

Detection of Electric Motor Damage Through Analysis of Sound...

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Annual Conference of Industrial Electronics Society

作者： Waldemar Bauer Marta Zagórowska Jerzy Baranowski Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.

关键词： Accuracy Fault detection Neural networks Training data Robustness Bayes methods Wind turbines Signal representation Time-domain analysis Electric motors

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Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream

arXiv

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arXiv 2022年

作者： Wasala, Mateusz Kryjak, Tomasz Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors use deep convolutional neural networks (e.g., the YOLO - You Only Look Once - family), which, however, due to their high computational complexity, are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on AMD Xilinx Zynq UltraScale+ MPSoC (Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition, 3840 x 2160 pixels) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems. © 2022, CC BY.

关键词： System-on-chip

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Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream

TechRxiv

引用

TechRxiv 2022年

作者： Wasala, Mateusz Kryjak, Tomasz Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors use deep convolutional neural networks (e.g., the YOLO – You Only Look Once – family), which, however, due to their high computational complexity, are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on AMD Xilinx Zynq UltraScale+ MPSoC (Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD – Ultra High Definition, 3840 x 2160 pixels) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems. © 2022, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm

arXiv

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arXiv 2022年

作者： Kowalczyk, Marcin Kryjak, Tomasz Department of Automatic Control and Robotics Agh University of Science and Technology Krakow Poland

关键词： Field programmable gate arrays (FPGA)

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Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm

TechRxiv

引用

TechRxiv 2022年

作者： Kowalczyk, Marcin Kryjak, Tomasz Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Neuromorphic vision is a rapidly growing field with numerous applications in the perception systems of autonomous vehicles. Unfortunately, due to the sensors working principle, there is a significant amount of noise in the event stream. In this paper we present a novel algorithm based on an IIR filter matrix for filtering this type of noise and a hardware architecture that allows its acceleration using an SoC FPGA. Our method has a very good filtering efficiency for uncorrelated noise – over 99% of noisy events are removed. It has been tested for several event data sets with added random noise. We designed the hardware architecture in such a way as to reduce the utilisation of the FPGA’s internal BRAM resources. This enabled a very low latency and a throughput of up to 385.8 MEPS million events per second. The proposed hardware architecture was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the Mercury+ XU9 module with the Mercury+ ST1 base board. © 2022, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Neural Network Regression Analysis of Magnetic Sensor Data for Spatial Magnet Positioning

Neural Network Regression Analysis of Magnetic Sensor Data f...

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Electrical, Electronic and Computing Engineering (IcETRAN), International Conference on

作者： Vladimir Sibinović Mirko Raković Department of Automatic Control Faculty of Electronic Engineering University of Niš Niš Serbia Chair of Mechatronics Robotics and Automation Faculty of Technical Science Novi Sad Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

This paper deals with the magnet position estimation based on the measurements from multiple Hall sensors. Estimation of the position is obtained from trained artificial neural networks. Data are gathered using a custom sensor board that has eight three-axis Hall sensors on it, and correlation between magnetic field and position was determined to be able to estimate position in X and Y directions. First measurements were made to determine the optimal height of the magnet for later data gathering. The outcome of the research was evaluated by comparing the ground truth with the estimated values of the best-trained network to determine the error of estimation. Precise measurement of the position of the magnet is intended to be used for the development of a tactile sensor that estimates the contact force through the displacement of the magnet in the deformable layer.

关键词： Magnetic field measurement Systematics Magnetic sensors Layout Estimation Tactile sensors Artificial neural networks

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

arXiv

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arXiv 2024年

作者： Bauer, Waldemar Zagórowska, Marta Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

关键词： Electric motors

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