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IEEE Conference on Decision and control

作者： Michelle S. Chong Department of Mechanical Engineering Control Systems Technology section Eindhoven University of Technology

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Entropy notions for ε-incremental practical stability and incremental stability of deterministic nonlinear systems under disturbances are introduced. The entropy notions are constructed via a set of points in state space which induces the desired stability properties, called an approximating set. We provide conditions on the system which ensures that the approximating set is finite. Lower and upper bounds for the two estimation entropies are computed. The construction of the finite approximating sets induces a robust state estimation algorithm for systems under disturbances using quantized and time-sampled measurements.

关键词： Upper bound Approximation algorithms Entropy Nonlinear systems State estimation

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Quantification of valve stiction in control loops using adaptive differential evolution algorithm

Quantification of valve stiction in control loops using adap...

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2024 China Automation Congress, CAC 2024

作者： Yang, Dong Lang, Xun Wu, Jiande Yu, Wanke Liu, Songhua Xie, Lei Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies. © 2024 IEEE.

关键词： Adaptive algorithms

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2024 China Automation Congress, CAC 2024

作者： Lang, Yumin Wu, Jiande Lang, Xun Yu, Wanke Yang, Chuanyan Su, Hongye Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Oscillation is a significant indication of the deterioration of control system performance and needs to be carefully monitored to ensure safe and stable process operations. However, industrial processes are often prone to noisy artifacts and nonstationary trends, resulting in low signal-to-noise ratios. In order to tackle this problem, this study introduces a novel framework for enhanced trend removal and noise reduction, which is featured by the following steps:(i) The circulant singular spectrum analysis (CiSSA) is used to decompose oscillation data into several reconstructed components (RCs). (ii) The estimated power spectral density (PSD) ofthese RCs is then smoothed using the Savitzky-Golay filter (SGF) to eliminate noise and identify the RCs corresponding to oscillatory components. (iii) These identified RCs are subsequently reconstructed to produce purified oscillatory data. The effectiveness of the suggested approach is validated using simulations and real-world industrial case studies. The findings indicate that the method notably enhances the quality of oscillatory data. © 2024 IEEE.

关键词： Power spectral density

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Measurement-Based control for Minimizing Energy Functions in Quantum systems

arXiv

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arXiv 2023年

作者： Clausen, Henrik Glavind Rahman, Salahuddin Abdul Karabacak, Özkan Wisniewski, Rafal Section for Automation and Control Department of Electronic Systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

In variational quantum algorithms (VQAs), the most common objective is to find the minimum energy eigenstate of a given energy Hamiltonian. In this paper, we consider the general problem of finding a sufficient control Hamiltonian structure that, under a given feedback control law, ensures convergence to the minimum energy eigenstate of a given energy function. By including quantum non-demolition (QND) measurements in the loop, convergence to a pure state can be ensured from an arbitrary mixed initial state. Based on existing results on strict control Lyapunov functions, we formulate a semidefinite optimization problem, whose solution defines a non-unique control Hamiltonian, which is sufficient to ensure almost sure convergence to the minimum energy eigenstate under the given feedback law and the action of QND measurements. A numerical example is provided to showcase the proposed methodology. © 2023, CC BY-NC-ND.

关键词： Hamiltonians

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Laundry Cluster Management Using Cloud 18

Laundry Cluster Management Using Cloud

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18th Conference on Computer Science and Intelligence systems, FedCSIS 2023

作者： Salach, Mateusz Trybus, Bartosz Pawlowicz, Bartosz Hubacz, Marcin Department of Complex Systems Rzeszow University of Technology Al. Powstancow Warszawy 12 Rzeszow35-959 Poland Department of Computer and Control Engineering Rzeszow University of Technology Al. Powstancow Warszawy 12 Rzeszow35-959 Poland Department of Electronic and Telecommunications Systems Rzeszow University of Technology Al. Powstancow Warszawy 12 Rzeszow35-959 Poland

ISBN: (纸本)9788396744784

electronic devices in the 21st century have numerous network components, including wireless or wired Internet access modules. Connecting devices to networks and cloud services enables them to access new functionalities and unlock system updates and device security enhancements. The article presents the concept of an intelligent laundry management system based on RFID and cloud computing. The Internet connection not only unlocks additional features of the washing machine, such as different washing modes, but also allows for selecting the appropriate detergent level and washing parameters based on the textile material being washed. Additionally, the paper presents the solution and measurement studies on the accuracy of textile identification. © 2023 Polish Information Processing Society.

关键词： Washing

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Tomasz Marciniak Agnieszka Stankiewicz Przemysław Zaradzki Department of Control Robotics and Electrical Engineering Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Poznan University of Technology Poznan Poland

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author’s publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score).

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A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping controller for Power System with Time-delay and Packet Dropout

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping ...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Cao, Di Zhang, Guozhou Hu, Jiaxiang Hu, Weihao Huang, Yuehui Chen, Zhe State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Aalborg University Department of Energy Technology Aalborg Denmark

ISBN: (纸本)9798350321173

This paper proposes a deep reinforcement learning (DRL) based adaptive sparsity promoting wide-area damping controller for power system. Different from traditional fixed wide-area control structure, the proposed method employs DRL algorithm to identify the optimal sparsity promoting control structure and obtain corresponding parameter settings under different operating conditions considering the time-delay and packet drop. Simulation are carried out on 10-machine 39-bus system to demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Time delay

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Effective-aggregation Graph Convolutional Network for Imbalanced Classification 19

Effective-aggregation Graph Convolutional Network for Imbala...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, Kefan An, Jing Kang, Qi Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Shanghai Institute of Technology The Department of Electrical and Electronic Engineering Shanghai China Tongji University The Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9781665472432

Classification is a common task and can be achieved by learning a predictive model from a labeled training dataset. However, the imbalanced data distribution makes the model tend to favor the majority class, which reduces the classification performance. Unlike traditional classification models, graph convolutional networks (GCNs) can extract useful feature information from unlabeled data. In this paper, a novel framework for imbalanced classification named effective-aggregation graph convolutional network (EGCN) is proposed. First, a graph generator constructs graph-structured data using both labeled and unlabeled data. Then, an aggregation control unit (ACU) is performed to improve the effectiveness of aggregation. ACU uses local estimation density to limit the aggregation of inter-class edges from a local perspective, and it enhances the aggregation of the minority class from a global perspective based on the imbalance ratio. Finally, the prediction results are obtained by a graph convolutional network. Experimental results on several real-world datasets show that EGCN has promising performance. © 2022 IEEE.

关键词： Convolution

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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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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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