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Electrical tree degradation of MgO/epoxy resin composites at different voltage frequencies

High Voltage 2024年第3期9卷 581-590页

作者： Yunxiao Zhang Wenxin Lin Yuanxiang Zhou Weiwei Xing Jiayu Cheng Chenyuan Teng College of Electrical Engineering and Automation Fuzhou UniversityFujian Key Laboratory of New Energy Generation and Power ConversionFuzhouChina State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina

Electrical tree degradation is one of the main causes of insulation failure in high-frequency *** tree degradation is studied on pure epoxy resin(EP)and MgO/EP composites at frequencies ranging from 50 Hz to 130 *** results show that the tree initiation voltage of EP decreases,while the growth rate and the expansion coefficient increase with ***,the bubble phenomenon at high frequencies in EP composites is *** with trap distribution character-istics within the material,the intrinsic mechanism of epoxy composites to inhibit the growth of the electrical tree at different frequencies is *** can be concluded that more deep traps and blocking effect are introduced by doping nano-MgO into EP bulks,which can improve the electrical tree resistance performance of EP composites in a wide frequency range.

关键词： composites tree Electrical

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Discretization-Chattering-Free Implementation of Arbitrary-Order Sliding Mode controllers 63

Discretization-Chattering-Free Implementation of Arbitrary-O...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Andritsch, Benedikt Watermann, Lars Koch, Stefan Reichhartinger, Markus Reger, Johann Horn, Martin Institute of Automation and Control Graz University of Technology Graz Austria Technische Universität Ilmenau Control Engineering Group Ilmenau Germany

ISBN: (纸本)9798350316339

In this paper an implementation of sliding mode control for disturbed integrator chains that does not suffer from the discretization chattering effect is presented. The method is based on an Euler forward discretization of the sliding mode controller. In a vicinity of the origin, that properly scales with the discretization time, the controller is altered to eliminate discretization chattering which can also improve the control accuracy. Finite-time stability of the origin is proven in the absence of a disturbance. In the disturbed case boundedness of the control error is shown. The control actuation is shown to be locally bounded. Exemplary applications to first- and second-order sliding mode control show the effectiveness of the method. © 2024 IEEE.

关键词： Sliding mode control

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Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

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Computer Modeling in engineering & Sciences 2023年第1期134卷 221-239页

作者： Tianyuan Jia Xiangyong Chen Xiurong Yao Feng Zhao Jianlong Qiu School of Automation and Electrical Engineering and Key Laboratory of Complex Systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyi276005China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and School of AutomationChina University of GeosciencesWuhan430074China

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this *** continuous and discontinuous activations are considered *** the mixed delays which are closer to reality are taken into the ***,two kinds of control schemes are proposed,including feedback and adaptive control *** on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are ***,the upper bound of settling time(ST)which is independent of the initial values is ***,the feasibility of our theory is attested by simulation examples.

关键词： Memristor-based neural networks fixed-time synchronization mixed delays discontinuous activations adaptive control

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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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Size Matters: Exploring the Impact of Scaling on Quadruped Robot Dynamics 25th

Size Matters: Exploring the Impact of Scaling on Quadruped R...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Rahvar, Faraz Yilmaz, Abdurrahman Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031720611

Quadruped robots are indispensable for specialized tasks, particularly in disaster scenarios like earthquakes, where their mobility surpasses that of fixed robots. However, altering their dimensions significantly impacts their mechanical requirements and control systems. This study investigates and compares the principal mechanical parameters affecting the control systems of quadruped robots during scaling, aiming to understand how scalability influences their design across different sizes. Using a standard quasi-direct driven commercial quadruped robot1(Unitree A 1) model featuring dual coaxial motor design and serial link architecture actuators, simulations are conducted at four different sizes, each expanding the robot size by 30%. These simulations analyze static and dynamic forces as the robot walks on a flat surface at a constant speed. To fortify our findings, simulations employ three trajectory scenarios, in each scenario, four robots are scaled in length, increasing by a coefficient of 0.3 from robot 1 to robot 4. The first and second scenarios feature different trajectory lengths, while the third scenario increases the trajectory height while maintaining the length of the second scenario. Specifically, we examine the required torque, energy consumption of the robot motors, reaction forces between the robot feet and terrain, and the mechanical cost of transport. These outcomes encompass critical components of the robot from a mechanical analysis perspective, including joints, providing valuable references for various actuator architecture designs. Our findings reveal a logical pattern in the increase of torques, power consumptions, and reaction forces as the robot scales. These insights lay the groundwork for developing a scalable control architecture for legged robots. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multipurpose robots

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Segmented Real-time Dispatch Model and Stochastic Robust Optimization for Power-gas Integrated Systems with Wind Power Uncertainty

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1480-1493页

作者： Ying Wang Kaiping Qu Kaifeng Zhang Key Laboratory of Measurement and Control of CSE Ministry of EducationSchool of AutomationSoutheast UniversityNanjingChina School of Electrical Engineering China University of Mining and TechnologyXuzhouChina

This paper develops a segmented real-time dispatch model for power-gas integrated systems(PGISs), where power-to-gas(P2G) devices and traditional automatic generation control units are cooperated to manage wind power uncertainty. To improve the economics of the real-time dispatch in regard to the current high operation cost of P2Gs, the wind power uncertainty set is divided into several segments, and a segmented linear decision rule is developed, which assigns adjustment tasks differently when wind power uncertainty falls into different segments. Thus, the P2G operation with high costs can be reduced in real-time adjustment. Besides, a novel segmented stochastic robust optimization is proposed to improve the efficiency and robustness of PGIS dispatch under wind power uncertainty, which minimizes the expected cost under the empirical wind power distribution and builds up the security constraints based on the robust optimization. The expected cost is formulated using a Nataf conversion-based multi-point estimate method, and the optimal number of estimate points is determined through sensitivity analysis. Furthermore, a difference-ofconvex optimization with a partial relaxation rule is developed to solve the non-convex dispatch problem in a sequential optimization framework. Numerical simulations in two testing cases validate the effectiveness of the proposed model and solving method.

关键词： Power-gas integrated system robust optimization real-time dispatch multi-point estimate method differenceof-convex optimization

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Reinforcement learning for optimal tracking of large-scale systems with multitime scales

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Science China(Information Sciences) 2023年第7期66卷 5-29页

作者： Jinna LI Hao NIE Tianyou CHAI Frank L.LEWIS School of Information and Control Engineering Liaoning Petrochemical University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute University of Texas at Arlington

This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A considerable contribution of this paper is the development of a data-driven SP-based RL method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral RL algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.

关键词： reinforcement learning singular perturbation multitime scales data-driven control

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Robust Stability and Stabilizability Conditions for Time-Delay Systems Under Stochastic Uncertainties

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IEEE Transactions on Automatic control 2024年第6期70卷 4037-4044页

作者： Chen, Jianqi Mao, Qi Zhao, Di Chen, Chao Nanjing University Center for Advanced Control and Smart Operations Suzhou China Nanjing Normal University School of Electrical and Automation Engineering Nanjing China Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Shanghai China Leuven3001 Belgium

This study first explores the mean-square robust stability problem of stable continuous-time linear time-invariant systems subject to stochastic multiplicative uncertainties with prescribed variance bounds. The internal structures of uncertainties, however, are not presumed to cope with diverse random noises and errors arising from networked channels. A necessary and sufficient mean-square stability condition is obtained involving a novel small-gain type characterization. Next, we consider the output feedback controller synthesis problem of networked control systems facing stochastic multiplicative uncertainties and intrinsic channel-wise time delays simultaneously. Based on the obtained mean-square stability condition, further, we develop a fundamental necessary and sufficient condition of mean-square stabilizability explicitly. Such a condition equivalently determines that an open-loop unstable system can be stabilized by output feedback in the mean-square sense. Finally, an analysis of delay robustness is elaborated, in which the mean-square stabilizability condition is extended to the case tolerating uncertain but upper-bounded time delays. Overall, this study provides a comprehensive analysis of the robust mean-square stability and mean-square stabilizability of time-delay systems under stochastic uncertainties, which can have practical implications for networked control systems. © 1963-2012 IEEE.

关键词： Continuous time systems

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Bearing fault diagnosis in gas turbine generators through MSP data-enhanced deep pyramidal residual networks

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International Journal of Data Science and Analytics 2025年 1-14页

作者： Xu, Xiaozhuo Li, Zhiyuan Zhao, Yunji Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment School of Electrical Engineering & Automation Henan Polytechnic University Jiaozuo454003 China Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment School of Electrical Engineering & Automation Henan Polytechnic University Jiaozuo454003 China

Accurate diagnosis of bearing faults in gas turbines is essential to ensure the proper functioning of the system. However, the harsh operating conditions of gas turbines result in fault data characterized by high dimensionality, strong coupling and noise. In real-world scenarios, especially when faced with limited fault samples, traditional methods are hampered by these factors while striving for optimal diagnostic accuracy. Furthermore, simply increasing the network depth to improve decoupling capabilities can lead to the disappearance or explosion of gradients in deeper network layers. To overcome these challenges, this study proposes a method that combines MSP data enhancement for preprocessing with pyramidal bottleneck residual networks for fault diagnosis. Specifically, the method uses mix-up and random scaling to augment the data, followed by principal component analysis to extract the principal components. The augmented samples are then fed into pyramidal bottleneck residual networks for training. Primarily, the method enhances defect features through preprocessing, allowing the model to establish smoother decision boundaries and improved generalization within the constraints of limited samples. Subsequently, the specialized structure of the pyramidal bottleneck residual networks enhances decoupling capabilities and reduces computational complexity. The effectiveness and superiority of the proposed method are demonstrated through algorithm validation on the CWRU and XJTU public datasets, together with practical engineering verification using BaiChuan gas turbine bearing monitoring data. The method is shown to have high practical value. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 206-217页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common *** from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific ***,we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement *** exploit collective milling structures of natural herds to cage the targets,and adopt a switching control policy inspired by sperms’chiral dynamics to optimize the trajectories of individuals,through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical,adaptive and scalable *** exists a critical swarm size,beyond which the excessive competition between agents would generate large mechanical forces,leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple *** work provides physical insights into distributed active swarms and could offer a multilevel,decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices,machine immunity,and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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