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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Tytiuk, Valerii Busher, Victor Kalinin, Alexey Chornyi, Oleksii Druzhinin, Valeriy Halushko, Dmytro Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Informative computer systems Abylkas Saginov Karaganda Technical University NPJSC Karaganda Kazakhstan Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Power Engineering Karaganda Industrial University NPJSC Temirtau Kazakhstan Dept. of Electrical Engineering Ukrainian State University of Science and Technologies Dnipro Ukraine

ISBN: (纸本)9798350395532

The paper is devoted to study of the influence of rolling modes on the performance of powerful interconnected electric drives of a hot rolling mill under the action of an electromagnetic coupling circuit between the electric drives of the roughing and finishing groups. The action of the electromagnetic coupling circuit manifests itself in the form of the influence of impact loads of synchronous electric drives on the angular velocity of direct current electric drives of the finishing group of the mill. The parameters of the electromagnetic coupling circuit are determined by the power supply scheme and equipment parameters. Values of impact loads applied to a synchronous motor depend on the rolling process parameters, in particular, the chemical composition of the rolled steel and the temperature of the ingot. The paper presents the results of calculating the rolling power for various rolling mode parameters. Using mathematical modeling in Simulink, numerical estimates of the influence of rolling modes on the magnitude of the voltage drop in the power supply unit and the drop in the angular velocity of the electric drive of the finishing group were obtained on the example of a wide-strip hot rolling mill 1700 of ArcelorMittal Temirtau JSC. © 2023 IEEE.

关键词： Hot rolling

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Resilient distributed optimization under mobile malicious attacks

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IFAC-PapersOnLine 2023年第2期56卷 997-1002页

作者： Yuan Wang Changxin Liu Hideaki Ishii Karl Henrik Johansson Department of Robotics Hunan University Changsha China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

关键词： Fault-tolerant distributed optimization multi-agent systems network security mobile adversary agents

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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A Data-Driven Distributed control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking

A Data-Driven Distributed Control Scheme: Learning Multi-Obj...

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International Conference on Intelligent Transportation

作者： Jiaming Zhong Reza Valiollahi Mehrizi Yash Vardhan Pant Amir Khajepour Department of Mechanical and Mechatronics Engineering Mechatronic Vehicle Systems (MVS) Lab University of Waterloo Waterloo ON Canada Department of Electrical and Computer Engineering Control Learning and Logic (CL2) group University of Waterloo Waterloo ON Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers, such as differential braking and torque vectoring, where controllers are regarded as distributed agents contributing to the same objective. However, this scheme is challenging in handling multiple conflicting objectives with coupled agents. A common approach for such tasks is the integrated MPC, where all objectives and agents are stacked together in one optimization. Nevertheless, as more agents and objectives are involved, the integrated MPC will face challenges like computational burdens and maintenance difficulties in practice. To this end, this paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency. First, under the assumption of information exchange, a multi-objective AMPC tailored from the alternating direction method of multipliers (ADMM) is proposed to decouple the system and achieve the same performance as the integrated scheme iteratively. Second, a learning-based method for initializing iterations is proposed to accelerate convergence. In addition, a data management method is proposed for real-time efficiency, and an authentication module is designed for learning reliability. We compare the proposed scheme against the integrated scheme via a combined path-tracking simulation for autonomous vehicles with various controllers. The proposed scheme achieves the same control performance as the integrated one while reducing the computational time by 43.5 %. Furthermore, the learning-based method saves 88.6% more computational time than without learning, making it suitable for real-time implementation.

关键词： Learning systems Torque Decentralized control Reliability engineering Real-time systems Convex functions Planning Information exchange Convergence Predictive control

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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy Research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Mechatronic system for running-in and testing of mechanical transmissions in mining shovels

Mining Science and Technology (Russian Federation)

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Mining Science and Technology (Russian Federation) 2025年第1期10卷 75-83页

作者： Malafeev, Sergey I. Malafeeva, Alevtina A. Konyashin, Vladimir I. Novgorodov, Vladimir I. Joint Power Co. Ltd. Moscow Russia Department of Computer Engineering and Control Systems Vladimir State University named after A. G. and N. G. Stoletov Vladimir Russia Vladimir Russia

The mechanical transmissions of the hoist and drag winches, as well as the crowd mechanisms of mining shovels, operate under highly variable and shock cyclic loads. These severe operating conditions impose stringent quality requirements on both the manufacturing and assembly of such transmissions. The lifecycle management process for these gearboxes includes running-in and acceptance testing at the manufacturing plant, followed by diagnostics and condition monitoring during field operation. Test benches designed for these purposes must closely replicate the operating conditions experienced by the transmission in actual service. The developed test bench is designed to perform running-in (initial wear-in of mating surfaces) and quality control testing of hoist and crowd winch gearboxes installed in electric mining shovels of the following models: EKG-8US, EKG-10, EKG-12K, EKG-15M, EKG-18, EKG-20KM, and EKG-32R. These operations are carried out as part of the standard acceptance testing procedure. The test bench is equipped with DC excavator motors rated at 350 kW (drive motors) and 560 kW (loading motor). The hoist winch gearbox from the EKG-15M shovel serves as the torque multiplier. Two intermediate shafts are connected via an intermediate coupling, with the loading motor driving one of these shafts. Motor control is implemented using standard DC transistor converters with PWM control, identical to those used in mining shovels equipped with DC main drives. The running-in and testing processes are carried out using the mutual loading method, which enhances energy efficiency by allowing regenerative energy to be reused within the system. The test bench software is based on the Pulsar-7 information and diagnostics system, which supports the following operational modes: 1. Interactive manual control, enabling the operator to verify the functionality and calibration of test bench components. 2. Automatic control of the running-in and testing processes, where pre-programm

关键词： Acceptance tests

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Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Adaptive PI-Fuzzy Like control of a Stack Pneumatic Actuators Testbed for Multi-Configuration Small Scale Soft Robotics 6

Adaptive PI-Fuzzy Like Control of a Stack Pneumatic Actuator...

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6th International Conference on Manipulation, Automation, and Robotics at Small Scales, MARSS 2023

作者： Al Mhdawi, Ammar K. Wright, Nick Humaidi, Amjad J. Azar, Ahmad Taher Newcastle University Department of Electrical and Electronic Engineering Newcastle United Kingdom Edge Hill University United Kingdom Prince Sultan University Asscl Lab Saudi Arabia University of Technology Control and Systems Engineering Department Baghdad Iraq College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Egypt

ISBN: (纸本)9798350330397

It has become evident that pneumatic muscle-based robots are the most compliant of all soft and semi-soft robotic testbeds under investigation, despite the fact that the artificial muscle-based robot is the most challenging due to its hyperelastic behavior resulting from the constitutive elastic material. The purpose of this paper is to examine the development of a physical pneumatic actuation testbed and the application of control strategies for precision small scale soft robotics control. Moreover, an adaptive PI-Fuzzy like control is proposed for reference signal tracking. It is imperative to note that in this application, the controller's performance is determined by its ability to track a reference signal as well as its ability to minimize the absolute tracking error. The Fuzzy Logic controller (FLC) controller, including the nature of the membership functions and the rule base, is described in detail. Simulations and hardware testing have revealed that the adaptive PI-Fuzzy like controller outperforms the traditional FLC controller in terms of experimental results. Additionally, the actuation speed is controlled under different loading conditions. It is considered that the proposed testbed is capable of controlling and managing multi-configurations of medium to small scale soft-based robots efficiently and effectively. © 2023 IEEE.

关键词： controllers

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Guest Editorial: Advances in High Voltage DC systems/Grids control, Operation, and Protection

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High Voltage 2020年第5期5卷 499-500页

作者： Kumars Rouzbehi Ali Mehrizi-Sani Department of Systems Engineering and Automatic Control University of SevilleSpain. Bradley Department of Electrical and Computer Engineering Virginia TechUSA.

Interconnections between AC power systems,power transmission with higher efficiency,and aggregation and delivery of power from offshore wind farms to different AC regions are among the main drivers for the development of HVDC *** grids can result in lower demand variability,higher flexibility,superior electricity market management,and higher economic *** is expected that isolated HVDC systems transition to an HVDC grid that overlays the existing AC *** a substantial development also results in a number of critical technical challenges related to system control,operation,and protection,which should be properly and accurately *** Special Issue focuses on the recent achievements and advancements in overcoming these *** papers for publications in this Special Issue fall into four major topics:devices,control,operation,and protection.

关键词： properly operation offshore

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Sensor Fault Detection and Isolation in Autonomous Nonlinear systems Using Neural Network-Based Observers

Sensor Fault Detection and Isolation in Autonomous Nonlinear...

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European control Conference (ECC)

作者： John Cao M. Umar B. Niazi Matthieu Barreau Karl Henrik Johansson Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor measurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators.

关键词： Uncertainty Fault detection Noise Observers Numerical simulation Sensor systems Sensors

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