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Bearing Fault Numerical Model for the Closed-slot Rotor Submersible Motor

Chinese Journal of electrical engineering 2023年第2期9卷 84-93页

作者： Zhe Ke Bokai Guan Chong Di Jingwen Yan Xiaohua Bao School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China School of Electrical and Computer Engineering The Ohio State UniversityColumbus OH 43210USA

A numerical model of the bearing fault of a motor with a closed-slot rotor using the finite element method(FEM)is *** rotor’s radial motion can be regarded as static eccentric at the defect time points and healthy at other time *** frequency of the harmonic component is analyzed corresponding to bearing fault in stator current according to the radial movement of the motor ***,the relative permeability variation region is established to achieve the radial motion of the rotor with bearing ***,the relative permeability variation region is established in the health and static eccentric ***,the defect time points are estimated and the static eccentricity model by transient field is ***,the relative permeability of the variable region in the static eccentric model is imported into the variable region of the health model at the defect time *** simulation results show that the air gap flux density of the bearing fault model is different from that of the health model and static eccentric *** addition,the stator current contains harmonic components of the bearing *** analysis results prove the applicability of the proposed model.

关键词： Bearing fault finite element method relative permeability variation region closed-slot rotor stator current

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Fixed-time-synchronized control: a system-dimension-categorized approach

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Science China(Information Sciences) 2023年第7期66卷 177-194页

作者： Wanyue JIANG Shuzhi Sam GE Dongyu LI Institute for Future & Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Department of Electrical and Computer Engineering National University of Singapore School of Cyber Science and Technology Beihang University Shanghai Institute of Satellite Engineering

This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO systems are required to reach the desired value simultaneously within a fixed time *** MIMO system is categorized into two cases: the input-dimension-dominant and the state-dimensiondominant cases. The classification is defined according to the dimension of system signals and, more importantly, the capability of converging at the same time. For each kind of MIMO system, sufficient Lyapunov conditions for fixed-time-synchronized convergence are explored, and the corresponding robust sliding mode controllers are designed. Moreover, perturbations are compensated using the super-twisting technique. The brake control of the vertical takeoff and landing aircraft is considered to verify the proposed method for the input-dimension-dominant case, which shows the essential advantages of decreasing the energy consumption and the output trajectory length. Furthermore, comparative numerical simulations are performed to show the semi-time-synchronized property for the state-dimension-dominant case.

关键词： fixed-time-synchronized convergence sliding mode control perturbed MIMO systems

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Data-driven cooperative output regulation of multi-agent systems under distributed denial of service attacks

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Science China(Information Sciences) 2023年第9期66卷 5-20页

作者： Weinan GAO Zhong-Ping JIANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering Tandon School of Engineering New York University

This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.

关键词： cooperative output regulation adaptive dynamic programming learning-based control multi-agent systems cybersecurity

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective control

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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State Transition Modeling Method for Optimal Dispatching for Integrated Energy System Based on Cyber-Physical System

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1617-1630页

作者： Yi Yang Peng Zhang Can Wang Zhuoli Zhao Loi Lei Lai the Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station College of Electrical Engineering and New EnergyChina Three Gorges UniversityYichangChina the Department of Electrical Engineering School of AutomationGuangdong University of TechnologyGuangzhouChina

The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the *** method uses the physical,integration,and optimization layers as a three-layer modeling *** physical layer is used to describe the physical models of energy units in the *** the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are *** optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target *** simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.

关键词： Integrated energy system(IES) cyber-physical system(CPS) state transition modeling optimal dispatching

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Active disturbance rejection control for COVID-19 outbreak based on SEIR model

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International Journal of Systems, control and Communications 2024年第3期15卷 292-311页

作者： Zhang, Haonan Tan, Wen Yu, Mei School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

Public health and social measures (PHSMs) standardise the non-pharmacological intervention (NPIs) policies that countries around the world have implemented to curb the spread of COVID-19, and may also serve as a guide for the implementation of future complex health policies in response to new pandemics of infectious diseases. In this paper we aim to use feedback control to help decision-makers establish PHSMs, which can effectively constrain the spread of COVID-19 pandemic with minimal financial loss. The time-dependent susceptible-exposed-infectious-removed (SEIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimisation. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model. © 2024 Inderscience Enterprises Ltd.

关键词： Disturbance rejection

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Model-Based Online Adaptive Inverse Noncooperative Linear-Quadratic Differential Games via Finite-Time Concurrent Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4247-4257页

作者： Lin, Jie Wu, Huai-Ning Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Noncooperative differential games provide a basis for the study of coordination, conflict, and control for a single dynamical system with multiple players. Within the linear-quadratic differential games (LQDGs), the optimal feedback gain matrix and the weighting matrices of individual cost function depict each player's control policy and the tradeoff of various objectives, respectively. In this article, we investigate the inverse problem of LQDG with partial state observation via a model-based online method, i.e., recover the cost function of each player. First, a state observer is used to estimate the system state. Then, the feedback gain matrices of players are learned using a finite-time concurrent learning (FTCL)-based adaptive law relaxing the persistent excitation (PE) condition which is required in traditional adaptive estimation methods. With the learned feedback gain matrices, a semidefinite programming (SDP) problem with the quadratic objective function can be set up for determining the weighting matrices of the cost function. The applicability and effectiveness of the proposed method are demonstrated with a numerical example and a shared steering control simulation. © 2020 IEEE.

关键词： Cost functions

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and Information Technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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