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A Modular Soft Sensor for Centrifugal Pumps

IFAC-PapersOnLine 2024年第14期58卷 91-96页

作者： Sebastian Leonow Qi Zhang Martin Mönnigmann Automatic Control and Systems Theory Department of Mechanical Engineering Ruhr University Bochum Universitaetsstrasse 150 44801 Bochum Germany

Soft sensors experience an increasing interest in recent years, as they can replace expensive hardware meters and the required embedded computing hardware has become cheap and powerful. We report results for the implementation of a soft sensor for the flow rate estimation in centrifugal pumps that achieves root mean square errors of about 5%. The proposed soft sensor is based on generic models for the drive and hydraulic part of the pump to ensure widespread applicability. We show the soft sensor and the models it is based on can be parametrized with simple measurements. All theoretical considerations are corroborated with measurements on a real industrial pump in a laboratory setup. The results show that the proposed soft sensor is capable of providing reliable flow rate estimates in spite of plant model mismatch and uncertain hardware components.

关键词： Soft Sensor Flow Rate Estimation Centrifugal Pump Variable-Speed Drive

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New Hysteresis control Block for Shunt Active Power Filter for Harmonics Compensation Generated by CFLs with PQ Theory 1

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2nd International Conference on Green Energy Conversion System, ICGECS 2023

作者： Hajjej, Mohamed Sbita, Lassaad El Aroudi, Abdelali Mandal, Kuntal Research Laboratory of Process Energies Environment and Electrical Systems National Engineering School of Gabes University of Gabes Zrig Eddakhlania6029 Tunisia Department of Electronics Electrical Engineering and Automatic Control Universitat Rovira i Virgili Tarragona Spain

ISBN: (数字)9789819761487

ISBN: (纸本)9789819761470

This paper proposes a New Modulation Hysteresis control block for a three phase four wire Shunt Active Power Filter (SAPF) for currents harmonics compensation generated by Compact Fluorescents Lamps (CFLs). This study employs the PQ power control strategy. The performance of the SAPF is evaluated under both simple hysteresis controller and under the new modulation hysteresis controller conditions using the Matlab/Simulink platform. The findings simulation results demonstrate that the proposed new technique can improve the power quality of the main, and the total harmonic distortion (THD) optimization, which also confirms the effectiveness of the approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Harmonic analysis

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Trajectory Planning of Autonomous Mobile Robot using Model Predictive control in Human-Robot Shared Workspace 3

Trajectory Planning of Autonomous Mobile Robot using Model P...

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3rd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Chen, Jieming Chen, Xiang Liu, Steven The Hong Kong Polytechnic University Department of Electrical Engineering Hong Kong Hong Kong University of Kaiserslautern Institute of Control Systems Kaiserslautern Germany

ISBN: (纸本)9798350398410

This paper proposes an integrated framework of trajectory planning and control for autonomous mobile robots (AMRs) in an intra-logistic scenario, where humans and robots share the same indoor workspace. The proposed framework consists of perception and trajectory planning. To perceive human motion, information from the RGB-D camera is used to detect human positions. Then a model-based tracking approach is employed to track multiple people's motion and estimate their speed over a short time horizon. For trajectory planning, a Model Predictive control (MPC) based scheme is adopted to generate motor commands while taking into account energy efficiency, safety, and addressing human-aware proximity constraints. To convexify the nonlinear collision avoidance constraints, Sequential Convexification Programming is employed. The simulation and experimental results demonstrate that the proposed method can be implemented in real-time and efficiently avoid people in advance. © 2023 IEEE.

关键词： Model predictive control

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Computational Fluid Dynamics Analysis on the Improvement of Hybrid Savonius-Darrieus NREL S809 at Various Fluid Flows 4

Computational Fluid Dynamics Analysis on the Improvement of ...

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4th International Conference on High Voltage engineering and Power systems, ICHVEPS 2023

作者： Irawan, Elysa Nensy Sitompul, Sandro Yamashita, Ken-Ichiro Fujita, Goro Shibaura Institute of Technology Functional Control Systems Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Salesian Polytechnic Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

Among all types of vertical axis wind turbines, Savonius and Darrieus are the most popular to study. Savonius has a very large torque and requires a low wind velocity to start the rotation, but the efficiency is below 20%. Meanwhile, Darrieus is not capable of self-starting if the wind velocity is below 4 m/s, but the efficiency is the best among all types of vertical axis wind turbines, which is around 40-45%. So far, many researchers have applied the hybrid Savonius-Darrieus to combine their performances. However, the conventional hybrid Savonius-Darrieus cannot produce an optimal Power Coefficient at a high Tip Speed Ratio. In this study, an improvement in the performance of the hybrid Savonius-Darrieus has been analyzed using the Computational Fluid Dynamics method at various fluid flows. This improvement was carried out with the aim of obtaining a high Moment Coefficient and Power Coefficient in all turbine Tip Speed Ratios compared to the conventional hybrid Savonius-Darrieus. The hybrid model used is the 2-bladed Savonius as the internal rotor and the 3-bladed Darrieus NREL S809 as the external rotor with a rotor radius ratio of 0.5. Improvements were made by changing the turbine rotation mode from hybrid at a low Tip Speed Ratio to single Darrieus at a high Tip Speed Ratio. This method was tested in the wind velocity range of 3-7 m/s using the Realizable k-e turbulence model, enhanced wall treatment. As a result, the best performance occurs at a wind velocity of 7 m/s, which is a Moment Coefficient value of 110% better than single Darrieus at low TSR and a maximum Power Coefficient value of 50% better than conventional hybrid. © 2023 IEEE.

关键词： Computational fluid dynamics

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Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

TechRxiv

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TechRxiv 2024年

作者： Canzini, Ethan Auledas-Noguera, Marc Pope, Simon Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield United Kingdom Airbus Robotics

Within the realm of flexible manufacturing, fixture layout planning allows manufacturers to rapidly deploy optimal fixturing plans that can reduce surface deformation that leads to crack propagation in components during manufacturing tasks. The role of fixture layout planning has evolved from being performed by experienced engineers to computational methods due to the number of possible configurations for components. Current optimisation methods commonly fall into sub-optimal positions due to the existence of local optima, with data-driven machine learning techniques relying on costly to collect labelled training data. In this paper, we present a framework for multiagent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks. We demonstrate our approach on two representative aerospace components with complex geometries across a set of drilling tasks, illustrating the capabilities of our method;we will compare this against state of the art methods to showcase our method’s improvement at finding optimal fixturing plans with 3 times the improvement in deformation control within tolerance bounds. © 2024, CC BY.

关键词： Reinforcement learning

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LMI-based Decentralized Load Frequency control of a Hybrid Power System with a Virtual Synchronous Generator and Battery Storage

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IFAC-PapersOnLine 2023年第2期56卷 10910-10916页

作者： Abdulla Omaruddin Paul Trodden Nuclear & Power Atkins UK Department of Automatic Control and Systems Engineering The University of Sheffield UK

This paper proposes the participation of wind generation in the decentralized control of load frequency of a hybrid power system consisting of, in addition to wind generation, conventional generation and battery storage. The wind generation is modelled as a ‘Virtual Synchronous Generator (VSG)’ in a separate control area with its own virtual frequency. It has also been proposed to operate the wind generation system in a ‘de-loaded’ mode, thereby allowing it to take part in frequency regulation services. For the purposes of a decentralized control design, the overall system model is decomposed into three subsystems. Static state-feedback control gains are computed by posing the decentralized control problem as a set of linear matrix inequalities (LMIs) subject to structural and stabilizing constraints.

关键词： Optimal operation and control of power systems control of renewable energy resources control system design

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Holistic Self-Distillation with the Squeeze and Excitation Network for Fine-grained Plant Pathology Classification

Holistic Self-Distillation with the Squeeze and Excitation N...

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International Conference on Information Fusion

作者： Jingxuan Su Sean Anderson Lyudmila Mihaylova Department of Automatic Control & Systems Engineering University of Sheffield UK

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.

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Operation Analysis and Performance Evaluation of a Modular Step-Down DC-DC Converter 7

Operation Analysis and Performance Evaluation of a Modular S...

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7th IEEE International Conference on Smart Technologies in Power engineering and Electronics, STEE 2024

作者： Stepenko, Serhii Stala, Robert Waradzyn, Zbigniew Yatchenko, Yevhenii Prystupa, Anatoliy Chernihiv Polytechnic National University Electrical Engineering Information and Measurement Technologies Chernihiv Ukraine Agh University of Krakow Department of Power Electronics and Energy Control Systems Kraków Poland

ISBN: (纸本)9798331540999

The paper presents a comprehensive analysis of the design parameters, operational characteristics and performance evaluation of a modular step-down DC-DC converter. The converter is engineered for applications demanding high-output current, featuring an input voltage of 48 V and an output voltage of 1 V. When operating in the single-stage voltage regulator module (VRM), its modular architecture is optimized to minimize resistive losses. Noteworthy is the integration of an input capacitor divider to alleviate voltage stress of components. Additionally, special attention is given to the impact of parasitic parameters on the converter's performance. Through a meticulous examination of system topology, validated by observed current and voltage waveforms, and analytical comparison to classic interleave converters, this study elucidates the operational intricacies of the converter. The findings, anchored in the context of a MOSFET-based four-module converter rated for a maximum power output of 350 W at 1 V, provide valuable insights for the design and optimization of similar power conversion systems. © 2024 IEEE.

关键词： DC-DC converters

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Hybrid Computational Framework for Fault Detection in Coil Winding Manufacturing Process Using Knowledge Distillation

Hybrid Computational Framework for Fault Detection in Coil W...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Izhar Oswaldo Escudero-Ornelas Divya Tiwari Michael Farnsworth Ze Zhang Ashutosh Tiwari Department of Automatic Control and Systems Engineering (ACSE) Sheffield UK

This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine Learning (SML) algorithm. The conventional End of the Line (EoL) tests are insufficient in detecting faults during process resulting in increased manufacturing costs and lead times. The proposed methodology utilises a Knowledge Distillation (KD) approach to address the challenges associated with the technique and optimise the student model's performance by employing architecture search and data augmentation. Multiple SML algorithms were evaluated to determine their effectiveness in predicting faults during manufacturing. The random forest algorithm demonstrated superior performance due to its ability to handle complex data and identify the impact of interdependencies of process parameters on the final product quality. The method was validated by conducting physical experiments on a linear coil-winding machine, and the results indicated that the random forest algorithm has the potential to decrease simulation time from 2 minutes to less than a second. The proposed methodology has the potential to reduce manufacturing time, enhance stator quality, and ultimately improve their reliability and safety.

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