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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Kiani, Hossein Golshaeian, Sajad Nazari, Mohammad Hassan Gharehpetian, G.B. Hosseinian, Seyed Hossein Sarbazi, Jafar Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350329797

As the world's population grows, many nations grapple with the task of supplying sufficient energy. One effective way to manage and plan for this demand is through energy demand forecasting. In this research, we adopt a method employing machine learning algorithms to predict energy demand in various countries, both developed and developing, up to the year 2050. For our long-term forecast covering 2020 to 2050, two types of historical data are utilized: (i) energy consumption (EEC), and (ii) socio-economic indicators, such as Gross domestic product (GDP), energy import and export, and population. An Artificial Neural Network (ANN) based machine learning is employed utilizing 30 years' worth of socio-economic data (1991-2020). In this manner, the utilization of the ANN facilitates the prediction of long-term energy consumption (EEC) for the planning period spanning from 2020 to 2050. Furthermore, we present an optimization model designed to enhance the precision of our predictions. The outcomes from machine learning algorithms serve as input for our comprehensive model, implemented through ANN across various sections. Ultimately, our forecasts indicate a projected increase in electric energy consumption by 130.1% for Iran, 37.4% for Portugal, and 58.6% for the United States in 2050 compared to 2020. Significant distinctions exist between developing and developed economies with regard to the anticipated trends in Energy Efficiency Compliance (EEC). © 2024 IEEE.

关键词： Adversarial machine learning

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Online Residual Learning from Offline Experts for Pedestrian Tracking 63

Online Residual Learning from Offline Experts for Pedestrian...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlachos, Anastasios Tsiamis, Anastasios Karapetyan, Aren Balta, Efe C. Lygeros, John Eth Zürich Automatic Control Laboratory Department of Information Technology and Electrical Engineering Zürich8092 Switzerland Control and Automation Group Inspire ZürichAG 8005 Switzerland The Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350316339

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance. © 2024 IEEE.

关键词： Prediction models

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Computation of Discrete-Time Analytic Signals 29

Computation of Discrete-Time Analytic Signals

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29th International Conference on Information, Communication and Automation Technologies, ICAT 2023

作者： Dzafic, Dzenita Dzafic, Izudin University of Sarajevo Faculty of Electrical Engineering Sarajevo Department for Automatic Control and Electronics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350399837

Complex signals play an important role in signal processing and communication systems. This paper focuses on two types of complex signals: complex baseband and analytic bandpass signals. Analytic signals contain only the positive frequency components of the original signal and can be efficiently computed using the FFT algorithm. Complex baseband signals represent modulated signals and can be generated using techniques such as quadrature modulation or discrete complex downconversion. Analytic bandpass signals have a non-zero centered bandwidth and can be generated using the FFT method or by directly generating a complex bandpass signal. In both cases, the signal can be manipulated using complex mixers and filters. © 2023 IEEE.

关键词： FIR filters

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Application of Discrete-Time Analytic Signals in Power System Analysis 29

Application of Discrete-Time Analytic Signals in Power Syste...

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29th International Conference on Information, Communication and Automation Technologies, ICAT 2023

作者： Dzafic, Dzenita Dzafic, Izudin University of Sarajevo Faculty of Electrical Engineering Sarajevo Department for Automatic Control and Electronics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350399837

Power system analysis often involves studying the dynamics of oscillatory signals and transient events. Analytic signals provide a useful representation of real-meaured signals that contain both oscillatory and non-oscillatory components. This work examines the application of discrete-time analytic signals to analyze power system signals using Kung's and Prony's method. These methods are used to estimate the oscillatory modes present in power system signals and their associated parameters. The performance of the methods is evaluated on simulated and real power system data. The analytic signal-based techniques are shown to produce accurate results and offer benefits for automated power system monitoring and disturbance analysis. © 2023 IEEE.

关键词： systems analysis

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3D Trajectory Tracking of a Quad-Rotor Unmanned Aerial Vehicle via the First Order Sliding Mode control 9

3D Trajectory Tracking of a Quad-Rotor Unmanned Aerial Vehic...

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Salihbegovic, Almir Talic, Azra Osmic, Nedim Sokic, Emir University of Sarajevo Faculty of Electrical Engineering Department for Automatic Control and Electronics Sarajevo71000 Bosnia and Herzegovina

ISBN: (纸本)9798350311402

control design for multi-rotor aerial vehicles (MAVs) is quite challenging problem due to their nonlinearitles, unknown dynamics, parametric uncertainties, an underactuated property, a nonlinear coupling dynamics and external disturbances. This paper introduces a first order sliding mode control (FOSMC) for robust stabilization of an under-actuated quad-rotor unmanned aerial vehicle (UAV) operating in the presence of external disturbances. The proposed FOSMC guarantees a finite time convergence of the system trajectories to the sliding surface. Obtained simulations show that the FOSM based approach improves robustness properties compared with the concurrent techniques, and enhance tracking performance of the quad-rotor UAV exposed to external disturbances. © 2023 IEEE.

关键词： Sliding mode control

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Statistical Learning Theory for control: A Finite-Sample Perspective

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IEEE control systems 2023年第6期43卷 67-97页

作者： Tsiamis, Anastasios Ziemann, Ingvar Matni, Nikolai Pappas, George J. ETH Zürich Department of Information Technology and Electrical Engineering Zürich8092 Switzerland KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typically under the purview of control theory. While these algorithms have already shown tremendous promise in certain applications [1], there are considerable challenges, in particular, with respect to guaranteeing safety and gauging fundamental limits of operation. Thus, as we integrate tools from machine learning into our systems, we also require an integrated theoretical understanding of how they operate in the presence of dynamic and system-theoretic phenomena. Over the past few years, intense efforts toward this goal - an integrated theoretical understanding of learning, dynamics, and control - have been made. While much work remains to be done, a relatively clear and complete picture has begun to emerge for (fully observed) linear dynamical systems. These systems already allow for reasoning about concrete failure modes, thus helping to indicate a path forward. Moreover, while simple at a glance, these systems can be challenging to analyze. Recently, a host of methods from learning theory and high-dimensional statistics, not typically in the control-theoretic toolbox, have been introduced to our community. This tutorial survey serves as an introduction to these results for learning in the context of unknown linear dynamical systems (see 'Summary'). We review the current state of the art and emphasize which tools are needed to arrive at these results. Our focus is on characterizing the sample efficiency and fundamental limits of learning algorithms. Along the way, we also delineate a number of open problems. More concretely, this article is structured as follows. We begin by revisiting recent advances in the finite-sample analysis of system identification. Next, we discuss how these finite-sample bounds can be used downstream to give guaranteed performa

关键词： Learning algorithms

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LoRaWAN Sensors Integration for Manufacturing Applications via Edge Device Model with OPC UA 28

LoRaWAN Sensors Integration for Manufacturing Applications v...

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28th International Conference on Applied Electronics, AE 2023

作者： Peniak, Peter Holečko, Peter Bubeniková, Emilia Kanáliková, Alžbeta University of Žilina Žilina Faculty of Electrical Engineering Engineering and Information Technology Department of Control and Information Systems Slovakia University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350335538

The article deals with the integration of Lo(ng) Ra(nge) Wide Area Network (LoRaWAN) sensors with generic manufacturing applications and Industrial Internet of Things (IIoT) use-cases. The focus is on creating a suitable Edge device model that enables the integration of specific Long Range (LoRa) sensors together with the other generic Internet of Things (IoT/IIoT). The model is based on the Open Platform Communications Unified Architecture (OPC UA) standard, which is the most suitable integration platform for various control systems and manufacturing applications. The model was implemented in Node-RED software and subsequently validated to confirm the required integration of LoRaWAN sensors within OPC UA models. In contrast to other approaches based on integration gateway or LoRaWAN/OPC UA protocol convertors, the proposed model utilizes extended capabilities of Edge computing. It enables Edge processing and data transformation of LoRaWAN sensors data transferred by Message Queuing Telemetry Transport (MQTT) protocol from the network server, based on requirements of manufacturing applications for cloud solutions. © 2023 IEEE.

关键词： Wide area networks

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Design and Implementation of Safety Functions for the CNC Milling Machine 15

Design and Implementation of Safety Functions for the CNC Mi...

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15th International Conference Elektro, ELEKTRO 2024

作者： Brtis, Jozef Zdansky, Juraj Pirnik, Rastislav University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350372359

The paper deals with the design and implementation of safety functions for a CNC milling machine. It is a relatively small CNC milling machine that does not pose a great danger to trained persons. Because it is used in areas where even untrained persons can come into contact with it, it was necessary to implement a safety system that implements safety functions preventing accidents when entering the dangerous zone of the CNC milling machine. The safety system is implemented based on a safety PLC and the sensors are designed using a two-channel architecture to meet the required SIL (safety integrity level). © 2024 IEEE.

关键词： Security systems

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A Data-Driven Adaptive control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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