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3rd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Kumar, Anil Altaf, Aatif Kumar, Mahendra Alhussainy, Abdullah A. Alghamdi, Sultan Alobaidi, Abdulraheem H. Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar144008 India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350304732

The work addresses PID control design based on the velocity-pausing particle swarm optimization (VPPSO) technique. The suggested control design is utilized to develop a load frequency control (LFC) approach for an isolated microgrid (MG) with heat-pumps (HPs) and electric-vehicles (EVs). The efficiency and effectiveness of suggested control design are assessed when there are randomized solar power and load-demand disturbances. Furthermore, the robustness and effectiveness of suggested control schemes are evaluated under uncertainty in system parameters and different EVs & HPs controller's operating time-scheduling situations. The simulation results demonstrated the efficiency and usefulness of the recommended VPPSO-PID control strategy under several practical situations. © 2023 IEEE.

关键词： Electric Vehicles Load frequency control (LFC) Microgrid PID controller Velocity Pausing PSO (VPPSO)

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Cascaded Design for Finite-Time Trajectory Tracking control of an Underactuated Surface Vehicle 8

Cascaded Design for Finite-Time Trajectory Tracking Control ...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Chen, Ziyin Ge, Shuzhi Sam Beijing Institute of Space Mechanics & Electricity Department of Control Engineering Beijing China National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9781665484138

This paper discusses the cascaded design for finitetime trajectory tracking control of an underactuated unmanned surface vehicle (USV). The tracking error dynamics is established with the guidance of a virtual reference vehicle, and transformed into a new cascaded structure, so the control of the overall system can be equivalent to stabilization of two interconnected subsystems separately, which can facilitate the control design by verifying the properties of the interconnection instead of iterative constructing Lyapunov functions for the whole system. The nonsingular fast terminal sliding mode (NFTSM) method is applied to stabilize the kinematic and dynamic subsystems in the present of model uncertainties, which can drive the states to the origin in finite time and avoid the possible singularity during the control phase. The stability of the cascaded system is guaranteed by verifying the satisfaction of the sufficient conditions. Numerical simulation results are provided to demonstrate the effectiveness of the proposed cascaded design. © 2022 IEEE.

关键词： Unmanned surface vehicles

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems 19

Individually-guided Evolutionary Algorithm for Solving Multi...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, XiaoLing Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States

ISBN: (纸本)9781665472432

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants. © 2022 IEEE.

关键词： Evolutionary algorithms

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Recent Developments in Event-Triggered control of Nonlinear Systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Uncertainty-Aware Path Planning with Multiple Constraints for Autonomous Vehicles Based on Nonlinear Reduced-Order Model Predictive control

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IEEE Transactions on Consumer Electronics 2024年

作者： Badiei, Mohammad Hossein Dashtaki, Saeed Mohammadi Vafamand, Navid Piran, Md. Jalil University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Shiraz University Department of Power and Control Engineering Shiraz Iran Sejong University Department of Computer Science and Engineering Korea Republic of

In this paper, we present a Risk-Aware Nonlinear Reduced-Order Model Predictive control framework that utilizes real-time state estimation, reduced-order modeling, and optimization for reliable and efficient path planning with low energy consumption. Our method employs filtering techniques to estimate the system's state by correcting for process and measurement noise, providing an accurate representation of the vehicle's position and environment. A reduced-order model predicts future states based on the estimated input, while the optimizer adjusts control inputs to meet trajectory and safety constraints, such as avoiding obstacles. By incorporating adapted filtering techniques and optimized cost functions, our approach minimizes computational overhead while maintaining precise control over vehicle navigation. In the experiments, we introduce communication and process noises, allowing the robot to operate without knowing the precise location of obstacles and resulting in potential near-collisions. This involves redefining the constrained optimal control problem for a reduced-order model, addressing input constraints amidst uncertainties. Our method achieves over 94% success in collision-free navigation within the first 50 steps, while maintaining precise trajectory alignment and adapting to environmental changes. It also improves prediction speed up to 15% per cycle compared to NMPC and demonstrates 91% overall effectiveness in avoiding collisions and reaching the target. © 1975-2011 IEEE.

关键词： Cost functions

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Efficient Augmentation of Human Action Recognition Datasets with Warped Windows 26

Efficient Augmentation of Human Action Recognition Datasets ...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

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State of Charge Management for Grid-Connected PV with Supercapacitor System Considering LVRT Operation

State of Charge Management for Grid-Connected PV with Superc...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kritprajun, Paychuda Tolbert, Leon M. Sun, Jingjing Wang, Jingxin Praisuwanna, Nattapat Liu, Yunting Ferrari, Maximiliano The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States The Pennsylvania State University Department of Electrical Engineering University ParkPA United States Oak Ridge National Laboratory Grid Components and Control Oak RidgeTN United States

ISBN: (纸本)9798350316445

This paper presents a state of charge (SOC) management strategy to ensure low-voltage ride-through (LVRT) services of a grid-connected photovoltaic (PV) with a supercapacitor (SC) energy storage system (PVSS). A concept of SOC reservation for LVRT operations is introduced to guarantee that the PV grid-connected system can provide LVRT services complying with grid code requirements. SOC management is derived by considering SC limitations, the current rating of the converters, and grid conditions. Therefore, safe operation of the power system and PVSS can be achieved. The study also adopts an electrothermal model of the SC to adjust the current set points during SOC management by considering the temperature-dependent SC parameters;thus, both safety and full utilization of the SC can be achieved. Consequently, SOC management can rapidly be accomplished, thereby increasing the certainty of providing LVRT services to the system. © 2023 IEEE.

关键词： Grid-connected photovoltaic system low-voltage ride-through state of charge management supercapacitor

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