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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Marek Rydel Rafał Stanisławski Grzegorz Bialic Department of Electrical Control and Computer Engineering Opole University of Technology Opole Poland

The paper presents two ways of modeling fractional-order systems using delta and nabla definitions of Grünwald-Letnikov fractional-order difference. Finite-length approximation of fractional-order system are then applied for designing a linear-quadratic-estimator of fractional-order system. Predicted state-space vector are used by linear-quadratic-regulator. That approach enables simple determination of standard integer-order LQG controller for control of fractional-order system. The simulation experiments confirm that presented methodology is efficient and enables obtaining a good controller performance.

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Learning-Based State Estimation for Automated Lane-Changing 27

Learning-Based State Estimation for Automated Lane-Changing

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ha, Won Yong Chakraborty, Sayan Lin, Xiaoyi Ozbay, Kaan Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering 370 Jay Street BrooklynNY11201 United States C2SMARTER Center New York University Department of Civil and Urban Engineering 6 MetroTech Center BrooklynNY11201 United States

ISBN: (纸本)9798331505929

This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. © 2024 IEEE.

关键词： State estimation

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

arXiv

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arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Atmosphere-dependent combustion of Ganoderma lucidum biomass toward its enhanced transformability into green energy

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Environmental Science and Pollution Research 2024年第29期31卷 42372-42387页

作者： Cao, Hanlin Zhan, Hongda Qi, Jianying Lin, Sen Ren, Mingzhong Liang, Jiayu Evrendilek, Fatih He, Yao Liu, Jingyong Technical Centre for Soil Agriculture and Rural Ecology and Environment Ministry of Ecology and Environment Beijing100012 China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and Engineering Guangdong University of Technology Guangzhou510006 China Ministry of Ecology and Environment South China Institute of Environmental Sciences Guangzhou510275 China Department of Electrical and Computer Engineering College of Engineering Boston University BostonMA02215 United States

Globally, the circular efficiency of biomass resources has become a priority due to the depletion and negative environmental impacts of fossil fuels. This study aimed to quantify the atmosphere-dependent combustion of Ganoderma lucidum (GL) biomass and its thermodynamic and kinetic parameters toward enhancing its circularity and transformability characteristics. The GL combustion occurred in the three stages of moisture removal, volatile release, and coke combustion. Combustion performance characteristics were more favorable in the N2/O2 atmosphere than in the CO2/O2 atmosphere under the same heating rates. The rising heating rate facilitated the release of volatiles. According to the model-free methods of Ozawa-Flynn-Wall and Kissinger–Akahira–Sunose, the activation energies essential for the primary reaction were 283.09 kJ/mol and 288.28 kJ/mol in the N2/O2 atmosphere and 233.09 kJ/mol and 235.64 kJ/mol in the CO2/O2 atmosphere. The gaseous products of the GL combustion included CH4, H2O, C = O, CO, CO2, NH3, C = C, and C–O(H). Ash prepared in both atmospheres exhibited a tendency for slag formation, with oxy-fuel combustion lowering its risk. This study thus provides a theoretical and practical basis for transforming GL residues into a sustainable energy source. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Combustion

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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1176-1193页

作者： Xiaoling Wang Qi Kang MengChu Zhou Qi Deng Zheng Fan Haoyue Liu Department of Control Science and Engineering Tongji University Shanghai China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.

关键词： Learning systems Diversity reception Machine learning Learning (artificial intelligence) Multitasking Fifth Industrial Revolution Optimization Knowledge transfer Faces Convergence

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PV Grid Forecasting Analysis using Artificial Neural Network

PV Grid Forecasting Analysis using Artificial Neural Network

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International Middle-East Power System Conference, MEPCON

作者： Mohamed R. Aboelmagd Ali Selim Mamdouh Abdel-Akher Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt Department of Control and Computer Engineering Dep. Almaaqal University Basra Iraq

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison depends on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather; unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation

关键词： Renewable energy sources Accuracy Wind speed Time series analysis Production Artificial neural networks Power system stability Mathematical models Forecasting Wind forecasting

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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IEEE Conference on Decision and control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

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Enriching Physical-Virtual Interaction in AR Gaming by Tracking Identical Real Objects

arXiv

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arXiv 2025年

作者： Yu, Liuchuan Huang, Ching-I Wang, Hsueh-Cheng Yu, Lap-Fai Computer Science Department George Mason University FairfaxVA United States Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Electrical and Computer Engineering Department and Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Computer Science Department George Mason University FairfaxVA United States

Augmented reality (AR) games, particularly those designed for headsets, have become increasingly prevalent with advancements in both hardware and software. However, the majority of AR games still rely on pre-scanned or static scenes, and interaction mechanisms are often limited to controllers or hand-tracking. Additionally, the presence of identical objects in AR games poses challenges for conventional object tracking techniques, which often struggle to differentiate between identical objects or necessitate the installation of fixed cameras for global object movement tracking. In response to these limitations, we present a novel approach to address the tracking of identical objects in an AR scene to enrich physical-virtual interaction. Our method leverages partial scene observations captured by an AR headset, utilizing the perspective and spatial data provided by this technology. Object identities within the scene are determined through the solution of a label assignment problem using integer programming. To enhance computational efficiency, we incorporate a Voronoi diagram-based pruning method into our approach. Our implementation of this approach in a farm-to-table AR game demonstrates its satisfactory performance and robustness. Furthermore, we showcase the versatility and practicality of our method through applications in AR storytelling and a simulated gaming robot. Our video demo is available at: https://***/ rPGkLYuKvCQ. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational efficiency

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High-Value Target Escort and Defense Scenario with Differential Subgame Cost 63

High-Value Target Escort and Defense Scenario with Different...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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