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European control Conference (ECC)

作者： Kiran Kumar Sathyanarayanan Philipp Sauerteig Pablo Zometa Stefan Streif Automatic Control and System Dynamics Lab Technische Universität Chemnitz Chemnitz Germany Faculty of Engineering German International University Berlin Germany Department of Bioresources Fraunhofer Institute for Molecular Biology and Applied Ecology Giessen Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Growing crops in controlled-Environment Agriculture (CEA) farms, such as greenhouses and vertical farms, can help in meeting the demands of urban centers and achieving climate goals. Recently, many advanced control techniques like Model Predictive control (MPC) and its variants have been developed for energy-efficient operation and minimization of resource utilization. However, real-time implementation of these advanced strategies come along with certain computational hardware requirements, thus, increasing the operating costs. In this work, we propose to learn the MPC policy of a greenhouse control by means of a Deep Neural Network (DNN) in order to be implemented on a low-cost microcontroller. Additionally, we use a feedback law to reduce undesired quantization effects. The efficiency of our approach is exemplified by means of a simulation study for greenhouse control.

关键词： Quantization (signal) Microcontrollers Optimal control Greenhouses Artificial neural networks Minimization Real-time systems

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Monte-Carlo-based simulation and investigation of 230 kV transmission lines outage due to lightning

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High Voltage 2020年第1期5卷 83-91页

作者： Ehsan Karami Ahmad Khalilinia Afshar Bali Kumars Rouzbehi Electrical Engineering Department Sharif University of TechnologyTehranIran West Regional Electric Company KermanshahIran Department of System Engineering and Automatic Control University of Seville41004 SevilleSpain

Here,using the probabilistic evaluation based on the Monte Carlo method,back-flashover rate and shielding failure flashover rate of 230 kV overhead transmission lines in the western regions of Iran are *** such an aim,first,the number of thunderstorm days per year is collected from the reported weather information in order to determine the ground flash ***,using MRU-200 equipment,the tower-footing resistance of several towers is ***®software is used in order to produce lightning surges considering its probabilistic nature and randomly distribution on the ground to evaluate striking distance based on the geometric ***,calculated parameters are transferred to EMTP-RV software by establishing a link to perform the transient simulation and report the results for modelled 230 kV transmission ***,considering IEEE-1243 standard,it is shown that due to high ground flash density,using 230 kV tower with one shield wire is not sufficient to protect the line against lightning phenomena.

关键词： flash lightning probabilistic

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Geoecological Information-Modeling system and Its Applications 23

Geoecological Information-Modeling System and Its Applicatio...

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23rd International Conference on control systems and Computer Science Technologies, CSCS 2021

作者： Nitu, Costica Dumitrascu, Alexandru Krapivin, Vladimir F. Soldatov, Vladimir Yu University Politehnica of Bucharest Department of Automatic Control and System Engineering Bucharest Romania Kotelnikov's Institute of Radioengineering and Electronics Russian Academy of Sciences Fryazino Branch Fryazino Russia

ISBN: (纸本)9781665439398

New information technology is represented to be an effective tool for environmental monitoring data processing when unremovable information uncertainties exist and additional in-situ measurements can not be realized. This technology is based on combined using the GIS tools and mathematical models. Series of real polluted environment monitoring tasks are given as confirmation of the efficiency of developing this technology. © 2021 IEEE.

关键词： Decision making

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Research of Adaptive Attitude control Method Considering Disturbance Compensation and Fuzzy Parameter Adjustment

Research of Adaptive Attitude Control Method Considering Dis...

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Chinese Automation Congress (CAC)

作者： Xiaodong Liu Yu Zhang Yuye Lin Jiaxuan Mo JiKang Zhao Beijing Aerospace Automatic Control Institute Beijing China System Engineering Department China Academy of Launch Vehicle Technology Beijing China

Be directed against the characteristics with respect to faster change of aircraft parameters, higher uncertainty and stronger channel coupling, a robust adaptive design method of aircraft attitude controller is proposed. According to the attitude motion equations during unpowered flight, a nonlinear design model facing for attitude control is first established. Then, according to the as-built design model, a robust dynamic inversion control method based on adaptive multi-variable linear extended state observer is proposed, and it is applied to the design of aircraft full-channel attitude controller through dynamic surface control theory. At the same time, a fuzzy parameter adjustment strategy is proposed to adjust the gain coefficient real-timely along with the margin of the present control ability, which can ensure the matching relationship between control parameters and control ability. Through the analysis on the simulation results, in comparison with one conventional controller, the controller proposed in this research ensures that the aircraft can track the attitude angle command faster and more accurately, and it has strong robust characteristics against time-varying uncertainty.

关键词： Couplings Adaptation models Uncertainty Adaptive systems Attitude control Atmospheric modeling Observers

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Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background 10th

Real-Time Vehicle Detection Method Based on Aerial Image in ...

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10th China Conference on Command and control, C2 2022

作者： Wu, Chen Liang, Ruofei He, Shuangcheng Wang, Hongshuo Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Beijing Science and Technology Leike Electronic Information Technology Co. Ltd. Beijing China Beijing Institute of Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9789811960512

In the field of military and security, the vehicle target has high capture value because of its high mobility, large load and high threat. In recent years, with the development of UAV technology and the improvement performance, as well as the rapid development and wide application of deep convolutional neural network in the field of object detection, vehicle detection based on UAV has become an important task, which has application value and practical significance that cannot be ignored. Compared with the traditional ground monitoring system, the UAV has high mobility, the background of the aerial image is more complex, and the target scales vary widely. Therefore, based on deep convolutional neural network, this paper proposes a feature fusion method combining context, which enhances the feature extraction ability of small targets in complex background. Then, a multi-scale region proposal matching method based on location clustering is proposed to enhance the matching degree between target features and region proposal. Finally, this paper designs a lightweight backbone based on DenseNet [1], which effectively reduces the computational cost. Experimental results show that our model reached the accuracy of 86.7% on PASCAL VOC (only car and bus are selected) dataset, which has a 3.1% bonus compared with the baseline SSD [2]. Moreover, in order to show the effect on aerial images, an additional experiment is conducted on the UAV123 dataset, which gets a 5.7% bonus. The speed of our model has a 180 fps on the NVIDIA RTX 2080. © 2022, Chinese Institute of Command and control.

关键词： Unmanned aerial vehicles (UAV)

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Blockchain-based peer to peer energy trading using distributed model predictive control

Blockchain-based peer to peer energy trading using distribut...

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European control Conference (ECC)

作者： Manuel Sivianes Ascensió n Zafra-Cabeza Carlos Bordons Systems Engineering and Automatic Control Department School of Engineering Universidad de Sevilla Seville Spain System Engineering and Automation Department School of Engineering Universidad de Sevilla Seville Spain Systems Engineering and Automatic Control Department ENGREEN Laboratory of Engineering for Energy and Environmental Sustainability Universidad de Sevilla Seville Spain

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The energy network is experiencing a decentralizing process due to the recent inclusion of distributed energy resources (DERs), such as photovoltaics (PV), electric vehicles (EV), or batteries. This paradigmatic shift involves new challenges that require enhancing the electricity system's flexibility while preserving its integrity and stability. One way of dealing with them is by unbundling the electrical network into smaller, more manageable units, known as microgrids (MG). Nonetheless, the usage of distributed architectures leads to more exchanged data and control information between end agents, involving security or privacy issues. In this context, blockchain technology emerges as a feasible solution that promises transparent, tamper-proof, and safe systems in a decentralized ecosystem. This paper proposes a distributed energy management platform that takes full advantage of the blockchain technology to enable safe peer to peer (P2P) transactions. A Distributed model predictive control (DMPC) scheme is adopted, and its performance is compared with the centralized approach.

关键词： Photovoltaic systems Smart contracts Microgrids Prediction algorithms Stability analysis Blockchains Peer-to-peer computing

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Physics-informed neural networks for parameter learning of wildfire spreading

arXiv

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Data-driven Model Based Online Fault Detection Using OMP-ERR 4

Data-driven Model Based Online Fault Detection Using OMP-ERR

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Zhou, Guangze Luo, Zhong Zhu, Yunpeng Gao, Yi Wang, Zhiao Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Shenyang110819 China Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian116024 China University of Sheffield Department of Automatic Control and System Engineering SheffieldS13JD United Kingdom

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Model based online fault detection often conducted by extracting features from models driven by system input and output data under various working conditions. The efficiency of online system modelling is therefore significant to improve the performance of online fault detections. In this study, a novel fast data-driven modelling approach, known as the OMP (Orthogonal Matching Pursuit)- ERR (Error Reduction Ratio) method is proposed to improve the efficiency of online fault detections. The new system identification method is motivated by noticing that the traditional OMP algorithm is much faster but usually less accurate than the OLS (Orthogonal least squares) algorithm in the identification of system NARX (Nonlinear Auto-Regressive with Exogenous inputs) models. The problem is first illustrated by the identification of a Single Degree of Freedom (SDoF) system. After that, the OMP-ERR algorithm is developed to improve the NARX modelling efficiency for the purpose of system model-based online fault detections. A case study on the crack detection of a cantilever beam shows that the new approach is over 10 times faster than the traditional OLS modelling process, demonstrating the promising applications of the new approach in online fault detections in engineering practice. © 2022 IEEE.

关键词： Least squares approximations

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Fault quantifcation and mitigation method for energy management in microgrids using MPC reconfiguration

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IFAC-PapersOnLine 2022年第6期55卷 575-582页

作者： J.J. Marquez A. Zafra-Cabeza C. Bordons Department of System Engineering and Automatic Control. Universidad de Sevilla. Escuela Superior de Ingeniería. Camino de los Descubrimientos s/n 41092 Seville (SPAIN)

The current energy situation and the possibility of exhausting fossil fuels in a relatively near period, have led to investing efforts in the development of techniques that use renewable energy sources for power generation. A configuration that allows renewable energy sources to be integrated into the overall power system, advocates dividing the grid into distributed systems incorporating small-scale generation and storage. Microgrids are a well-known type of these systems. control systems help maintain the reliability of the energy supply while minimizing costs. In addition, it must be taken into account that faults can occur in the processes that make up the microgrid. In some cases, the control system can mask these faults, even allowing the fault to reach an irreparable level. In this context, fault-tolerant control is a tool that enables control objectives to be maintained even in the presence of faults. If necessary, the control objectives are adapted to the fault. Furthermore, the fault tolerant control system needs to be able to detect faults, quantify their intensity and act accordingly. In this way it is avoided that small faults, that in other circumstances would remain hidden by the control loop, cause faults of a greater magnitude. This article proposes a fault quantification method based on parity equations and structured residuals that, together with a fault accommodation tolerance mechanism, mitigates the consequences of possible faults in this type of system.

关键词： Microgrids Model Predictive control Fault tolerant control Fault quantification Accommodation

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Data-Driven Source Term Estimation of Hazardous Gas Leakages Under Variable Meteorological Conditions

SSRN

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SSRN 2024年

作者： Ni, Chuantao Lang, Ziqiang Wang, Bing Li, Ang Cao, Chenxi Du, Wenli Qian, Feng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies. © 2024, The Authors. All rights reserved.

关键词： Least squares approximations

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