检索结果-内蒙古大学图书馆

Controllability of heterogeneous networked sampled-data systems

Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

来源：评论

学校读者我要写书评

暂无评论

Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

来源：评论

学校读者我要写书评

暂无评论

Bolt-YOLO: Research on an Algorithm Framework for Detecting Bolt Defects in Transmission Lines

引用

IEEE Transactions on Power Delivery 2025年第3期40卷 1718-1729页

作者： He, Min Qin, Liang Deng, Xinlan Liu, Qing Liu, Kaipei Wuhan University Department of Electrical Engineering and automation Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy Wuhan430072 China

In transmission lines, bolt fittings are critical components that connect towers and insulators. These fittings are prone to defects, such as loosening or missing bolts, due to natural environmental factors, posing significant risks to the system's operation. Drone inspection images are increasingly used for the intelligent detection of bolt fittings, yet the small size of bolt components and the uneven distribution of sample quantities limit detection accuracy. To address these challenges, this paper proposes a multi-type bolt fitting defect detection algorithm aimed at improving detection accuracy and robustness. To tackle the issue of small target detection, we design a novel down-sampling method that incorporates both global and local feature perception, reducing the loss of small target information. This method is integrated into the Res-PANet, a multi-scale feature fusion structure based on residual skip connections, which compensates for the loss of small target details in high-level semantic features. Additionally, to address the sample imbalance problem, we introduce adaptive class weighting and a Minimum Peripheral Point Distance (MPPD) bounding box similarity constraint to better locate imbalanced sample regions from the loss function perspective. Experimental results, using a detection dataset containing four types of targets—normal bolts, missing pins, loose pins, and missing nuts—demonstrate the effectiveness of the proposed algorithm. Compared to the original YOLOv8 model, the improved model shows a 1.9% accuracy improvement for detecting missing pins, a 3.2% improvement for loose pins, and a 4.2% improvement for missing nuts. The overall mean accuracy increased from 69.5% to 72.1%. These results provide a solid technical foundation for detecting bolt fitting defects in transmission lines, offering significant practical value for future inspection tasks. © 1986-2012 IEEE.

关键词： Bolts

来源：评论

学校读者我要写书评

暂无评论

Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

引用

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

来源：评论

学校读者我要写书评

暂无评论

Full-Range Non-Resonant PWM Zero-Voltage-Switching Power Converters With Minimized Inductor Current Ripple

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年第2期13卷 2014-2026页

作者： Liu, Fei Ruan, Xinbo Yao, Kai Gu, Ling Yang, Fei Tse, Chi K. Nanjing University of Aeronautics and Astronautics Center for More-Electrical-Aircraft Power System College of Automation Engineering Nanjing211106 China Nanjing University of Science and Technology School of Automation Nanjing210094 China City University of Hong Kong Department of Electrical Engineering Hong Kong

Soft-switching techniques can greatly reduce the switching loss, thus improving the efficiency, power density, and reliability of power converters. Focusing on the non-resonant pulsewidth modulation (PWM) converters, this article proposes a novel approach to realize zero-voltage-switching (ZVS) with constant frequency in the full operation range while minimizing the current ripple. First, the basic quadrilateral current modulation unit (QCMU) is proposed, where an inductor is spanned across the midpoint of two half-bridge legs, so that the inductor current is shaped as quadrilateral with its minimum value constant. Based on that, a family of full-range non-resonant PWM ZVS converters, including dc-dc, dc-ac, and ac-dc converters, is generated. Then, the optimal operating mode and control scheme based on the combined PWM and phase-shift control for the proposed ZVS converters are presented. Experimental results of dc-dc converter, and simulation results of dc-ac inverter and ac-dc rectifier as an extension, are provided to verify the effectiveness of the proposed non-resonant PWM ZVS converters. © 2013 IEEE.

关键词： Resonant converters

来源：评论

学校读者我要写书评

暂无评论

A trajectory-level robot skill learning method based on fuzzy systems

引用

Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

来源：评论

学校读者我要写书评

暂无评论

Understanding a Type of Forced Oscillation in Grid-Forming and Grid-Following Inverter Connected systems

引用

IEEE Transactions on Power Electronics 2025年第8期40卷 11628-11640页

作者： Chen, Song Wang, Yang Tian, Zhen Xiao, Xianyong Xie, Xiaorong Gomis-Bellmunt, Oriol Sichuan University College of Electrical Engineering Sichuan Chengdu610065 China Wuhan University School of Electrical Engineering and Automation Wuhan430070 China Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing100084 China Technical University of Catalonia Electrical Engineering Department Barcelona08034 Spain

This paper investigates a novel oscillation phenomenon in systems with grid-forming (GFM) and grid-following (GFL) inverters. Unlike previous studies that primarily focus on small-signal stability, this work explores forced oscillations (FOs), where GFL inverters act as excitation sources, inducing oscillations in GFM inverters. Specifically, interharmonics generated by maximum power point tracking (MPPT) control trigger low-frequency oscillation modes in GFM inverters. To analyze this phenomenon, the paper first examines the power characteristics of the interharmonics injected by MPPT and leverages this understanding to develop a P-ω admittance model for GFM and GFL inverter connected systems. This model enables precise prediction of FO amplitudes and provides insights into the influence of factors such as the number of GFM inverters on oscillation behavior. To validate findings, hardware-in-the-loop experiments are performed to verify the observed behavior in a system consisting of photovoltaic farms with GFL control and battery energy storage systems with GFM control. Finally, merits and limitations of this work are explicitly highlighted to provide a balanced perspective. © 1986-2012 IEEE.

关键词： Hardware-in-the-loop simulation

来源：评论

学校读者我要写书评

暂无评论

Cepstrum Lifting and Coherent Averaging-based Instantaneous Frequency Estimation of Low-Amplitude Signal Components for Rotating Machinery

引用

IEEE Sensors Journal 2025年第10期25卷 17479-17490页

作者： Liu, Shaokang Liu, Shuai Wu, Yingjie Liu, Weiliang Liu, Changliang Luo, Zhihong University of North China Electric Power Control and Computer Engineering Department Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China University of Northeast Electric Power Automation Engineering Department Jilin132000 China Inner Mongolia University of Technology Electric Power Department Hohhot010080 China

Instantaneous frequency (IF) serves multiple critical functions in the prognostic and health management of rotating machinery. Current IF estimation methodologies primarily concentrate on enhancing estimation accuracy at the algorithmic level. However, in practical industrial applications, the target signal component is often influenced by varying operating conditions, structural resonances, and background noise, causing it to become submerged or even disappear, ultimately resulting in the failure of conventional IF estimation techniques. To address these challenges, a novel IF estimation approach incorporating signal quality enhancement is developed. First, the complementary multi-source vibration signal is processed by the proposed segmented cepstrum lifting method to overcome the submersion of the resonant frequency band on the target component. Then, processed segmented signals are fused using coherent averaging technology to enhance the amplitude of the target component. Finally, leveraging the first-order approximation capability of the short-time fractional Fourier transform (STFrFT) for nonlinear frequency modulation modes, precise IF estimation is achieved. Verified by field data, the proposed method outperforms mainstream methods in both accuracy and applicability, highlighting its industrial application prospects. © 2025 IEEE.

关键词： Frequency estimation

来源：评论

学校读者我要写书评

暂无评论

Cataract Surgical Instruments Segmentation Free of Motion Blur Influence 24

Cataract Surgical Instruments Segmentation Free of Motion Bl...

引用

13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China Department of Aerospace Laser Technology and System Shanghai Institute of Optics and Fine Mechanics Shanghai China

ISBN: (纸本)9798400717956

Most of the existing cataract surgical instrument segmentation algorithms fail to consider the motion blur caused by the surgeon's hand movement or defocused camera. Most prior deblurring methods are oriented to visual tasks to merely recover the structure information from motion-blurred images. However, during surgical instrument segmentation, it is important to obtain the exact location of the surgical instruments at the current time to pave the way for subsequent downstream tasks, such as tracking and position estimation. In this work, we propose a spatiotemporal (ST) information-based network, consisting of the deblurring module and a subsequent segmentation module, for cataract surgical instrument segmentation free of motion blur influence to address the missing gap in the existing literature. The ST recurrent network with a series of ST-LSTM and gradient highway units removes the motion blur, which then enables for accurate semantic segmentation. The ST network model subsequently captures the temporal and spatial correlation for semantic segmentation. Experimental results quantitatively and qualitatively demonstrate that our method outperforms the state-of-the-art on our self-constructed dataset and the CaDIS benchmark dataset. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical systems Using Deep Learning Approach

引用

IEEE Transactions on automation Science and engineering 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Institute of Technology Department of Control and Simulation Center Harbin150001 China National Key Laboratory ofComplex System Control and Intelligent Agent Cooperation Harbin150001 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability. © 2004-2012 IEEE.

关键词： Factorization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：