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SSRN 2023年

作者： Zhao, Zhengang Cai, Shuiyan Li, Bo Li, Lei Luo, Chuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Green Energy Electric Power Measurement Digitalization Control and Protection Yunnan Kunming650500 China Electric Power Research Institute Yunnan Power Grid Co. Ltd Kunming650217 China

Inefficient heat dissipation in vapor chambers (VCs) is the major challenge in developing the vapor chamber. To study the effect of support columns on the maximum thermal power and thermal resistance of VCs, the relationship between the support column of the VC and the vapor pressure loss is analyzed. The relationship between the number of columns and the vapor channel pressure drop is obtained through numerical simulation and pressure drop test. The thermal performance test platform of the composite wick vapor chamber (CWVC) is built. The results show that the optimal filling ratios of the composite wick with three to six spiral support columns are 60%, 70%, 90%, and 70%, respectively, and the maximum thermal powers are 19.25 W, 25.61 W, 25.97 W, and 21.22 W, respectively. At power less than 24 W, SSC4-CWVC has more uniform temperature distribution and lower thermal resistance. At power over 27 W, SSC5-CWVC has the same characteristics. Besides, at 48W, the surface temperature difference of the condenser of SSC3-CWVC is 4.5℃, and the surface temperature difference between SSC4-CWVC and SSC5-CWVC is less than 3.0℃. Therefore, the SSC4-CWVC is suitable for low-power and SSC5-CWVC for higher-power applications. © 2023, The Authors. All rights reserved.

关键词： Drops

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment

Integration of ANN for Accurate Estimation and Control in Wa...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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Adaptive Fault Tolerant Tracking control of Heterogeneous Multi-Agent Systems with Non-Cooperative Target

SSRN

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SSRN 2022年

作者： Dong, Lijing Liu, Kaige Du, Shengli Yan, Hao Shen, Haikuo School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Beijing Jiaotong University Ministry of Education Beijing100044 China College of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China

In this paper, the fault tolerant tracking control problem of heterogeneous multi-agent systems(MASs) is studied, in which a non-cooperative target is considered. Since the target is non-cooperative, the system matrix of the target is unavailable. Therefore, the existing results dealing with consensus problem of heterogeneous multi-agent based on known exosystem matrix are inapplicable here. A neural-network-based adaptive observer is designed for estimating the output of the non-cooperative target. Then, a finite-time adaptive fault tolerant controller is developed to resolve the heterogeneous target tracking problem with actuator failures, with which the tracking errors converge within finite time. The proposed controller is fully distributed, which no longer requires the system matrix of the target and actuator fault rates of the tracking agents. Finally, the effectiveness of the developed strategies and the correctness of achieved stability conditions are verified by some numerical examples. © 2022, The Authors. All rights reserved.

关键词： Target tracking

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A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

TechRxiv

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TechRxiv 2022年

作者： Tang, Ning Chang, Jieling Wang, Lingling Fu, Li Neusypin, Konstantin A. Selezneva, Maria S. Peng, Linping The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Mathematics and System Sciences Beihang University Beijing100191 China

In practical applications, commercial-grade Micro-Electro-Mechanical System (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. © 2022, CC BY.

关键词： Gyroscopes

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Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme

arXiv

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arXiv 2021年

作者： Weibel, Jean-Baptiste Patten, Timothy Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Australia

While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial and real 3D data. We conjecture that the cause of those issue is the fact that many methods learn directly from point coordinates, instead of the shape, as the former is hard to center and to scale under variable occlusions reliably. We introduce spherical kernel point convolutions that directly exploit the object surface, represented as a graph, and a voting scheme to limit the impact of poor segmentation on the classification results. Our proposed approach improves upon state-of-the-art methods by up to 36% when transferring from artificial objects to real objects. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Modeling and fuzzy control of permanent magnet synchronous motors 10

Modeling and fuzzy control of permanent magnet synchronous m...

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10th International Conference on Power Electronics, Machines and Drives, PEMD 2020

作者： Juki, Domagoj-Kreimir Varga, Toni Beni, Tin Štil, Vedrana Jerkovi Baruki, Marinko Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781839535635

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic controller operation is tested using Real-Time (Model-in-the-Loop) simulations. © PEMD *** right reserved.

关键词： Permanent magnets

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Implementation of smart manufacturing maturity assessment framework: A socio-technical perspective

Implementation of smart manufacturing maturity assessment fr...

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2020 International Conference on Environment and Water Resources engineering, EWRE 2020

作者： Yue, Lei Wang, Zhou Fang, Yifang Han, Zhonghua Beijing100055 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang110016 China

Smart manufacturing capability maturity assessment has been widely carried out in order to help enterprises to identify the direction and route of smart transformation by stationing in enterprises. However, many maturity models for industrial enterprises can not reflect their industrial realities more accurately. Therefore, the aim of this paper attempts to make the smart manufacturing maturity assessment operational, and implement a specific assessment framework based on an existing general conceptual framework. This implementation involved two top-down approaches that one is socio-technical system as a top-level design of factors, and the other is refining assessment domains based on lifecycle and activity models. Results of this implementation showed a two-dimensional assessment framework instance and collections of definitions for capability maturity levels. To conclude, this implementation may be a reference for work specification of smart manufacturing maturity assessment. Three possible future research in the area was suggested at last. © The Authors, published by EDP Sciences, 2020.

关键词： Flow control

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Solar Radiation Forecasting Based on Random Forest and XGBoost

Solar Radiation Forecasting Based on Random Forest and XGBoo...

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International Conference on Green Technology and Sustainable Development (GTSD)

作者： Nhat-Truong Nguyen Thai Cat Tuong Dao Le Nhut Tuyen Nguyen Thanh-Dat Nguyen Minh-Vuong Le Isobel Timothea French Van-Tuong Doan Van Hoang Kim Vo Nghi C. Tran Duc-Hung Pham Van-Trung Nguyen Department of Renewable Energy Ho Chi Minh City University of Technology and Education Vietnam Department of Control Engineering and Automation Ho Chi Minh City Industry and Trade College Vietnam LILAMA2 International Technology College Dongnai Viet Nam Institute of Cognitive Neuroscience National Central University Taiwan College of Information and Communication Technology Can Tho University Viet Nam Faculty Electrical and Electronic Engineering Hung Yen University of Technology and Education VietNam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Renewable energy offers a viable alternative to depleting fossil fuels, contributing to global warming and greenhouse gas emissions. Thus, solar energy is widely favored as a renewable energy source. However, the output power of solar systems is unstable and is highly dependent on environmental circumstances such as radiation, humidity, temperature, and wind. Therefore, accurately estimating solar radiation is crucial for successful integration of the solar system into existing electrical grid systems. In this study, two different machine learning models, XGBoost and Random Forest, were used to predict solar radiation data in Ho Chi Minh City, Vietnam. The results show that RMSE and MAE of the Random Forest model (61.09 and 24.37) is smaller compared to XGBoost (61.31 and 25.08). Regression value of the two models is close to 0.955. In summary, this paper demonstrates that these two machine learning algorithms effectively forecast solar radiation, with the Random Forest model showing better performance.

关键词： Renewable energy sources Temperature distribution Temperature dependence Urban areas Humidity Predictive models Solar system

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