检索结果-内蒙古大学图书馆

International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Hu, Yufei Li, Weibing Deng, Youhan Jiang, Dingguo Huang, Meng Tian, Zhen Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Wuhan430014 China School of Electrical Engineering and Automation Wuhan University Wuhan430072 China Institute of Science and Technology China Three Gorges Corporation Beijing100038 China

ISBN: (纸本)9789819620791

To improve the small-signal stability of system, the damping torque model of flux-controlled grid-forming DFIG is constructed based on the small-signal model. And the low-frequency damping characteristics of system are analyzed by adopting the complex torque coefficient method. Then, analogous to the principle of PSS, a low-frequency oscillation damping method is proposed, which can efficiently optimize the low-frequency damping characteristics of system. Finally, based on the simulation results, the accuracy of theoretical analysis results and the effectiveness of proposed method are verified. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Damping

来源：评论

学校读者我要写书评

暂无评论

State Estimation of a Spatial 2-D Linear Diffusion Process With Mobile Sensors

引用

IEEE Transactions on automation Science and engineering 2025年 22卷 12024-12032页

作者： Zhang, Xiao-Wei Chen, Kui Wu, Huai-Ning Wang, Jin-Liang Wang, Zi-Peng Beijing University of Technology School of Information Science and Technology Beijing100124 China Tiangong University School of Control Science and Engineering Tianjin300387 China Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Beijing University of Technology School of Information Science and Technology Beijing Institute of Artificial Intelligence Beijing100124 China

This paper studies the state observer design of a spatial two-dimensional (2-D) linear diffusion process described by a linear parabolic partial differential equation (PDE) under mobile sensors. Firstly, we analyze the well-posedness of the PDE system and give the structure form of the state observer with mobile sensors. Subsequently, according to the number of mobile sensors, the 2-D space domain is divided into multiple sub-domains, and the mobile sensors are guided by the projection operator method, which can guarantee that the mobile sensors can only move in their respective 2-D sub-domains. Then, in the light of Lyapunov theory, Poincaré-Wirtinger inequality and Barbalat lemma, we propose an observation-plus-guidance design method to ensure the asymptotic stability of the state estimation error system. In the designed mobile strategy, the actual guidance of mobile sensors is essentially a physical synthesis of two direction guidance laws, where two dimensional guidance laws are designed separately. Moreover, the existence condition of the observer is given by linear matrix inequalities. At last, a numerical example is provided to demonstrate the efficacy of the proposed design scheme. © 2004-2012 IEEE.

关键词： Asymptotic stability

来源：评论

学校读者我要写书评

暂无评论

Computationally Efficient Optimal control Problem Formulation Illustrated by Automotive Applications 11

Computationally Efficient Optimal Control Problem Formulatio...

引用

11th Vienna International Conference on Mathematical Modelling, MATHMOD 2025

作者： Wegscheider, F. Galkina, A. Maier, J. Schlacher, K. Reichhartinger, M. Institute of Automation and Control Graz University of Technology Institute of Automation and Control Graz8010 Austria Magna Powertrain GmbH & Co KG Industriestraße 35 Lannach8502 Austria Engineering Center Steyr GmbH & Co KG Steyrer Straße 32 St. Valentin4300 Austria Lehrstuhl für Systemtheorie und Regelungstechnik Universität des Saarlandes Professur für Modellierung und Simulation Technischer Systeme Campus A5 1 Saarbrücken66123 Germany Institute of Automatic Control and Control Systems Technology Johannes Kepler Universität Linz Altenberger Straße 69 Linz4040 Austria

The paper focuses on reducing computational time when solving optimal control problems by expressing them within a flat coordinate system. To demonstrate and discuss this approach, two case studies are presented: Time-optimal velocity trajectory planning and torque control of permanent magnet synchronous motors. The reformulated optimal control problems show significant reductions in computational time without affecting the resulting trajectories. Additionally, analyses were performed to investigate the impact of prediction horizons and solver starting values on the observed computational advantages. The results clearly show the potential of the presented method for developing computationally efficient optimization-based control strategies. Copyright © 2025 The Authors.

关键词： Optimal control systems

来源：评论

学校读者我要写书评

暂无评论

Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

引用

Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Relative Localizability and Localization for Multirobot Systems

引用

IEEE Transactions on Robotics 2025年 41卷 2931-2949页

作者： Chen, Liangming Liang, Chenyang Yuan, Shenghai Cao, Muqing Xie, Lihua Southern University of Science and Technology School of System Design and Intelligent Manufacturing Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Carnegie Mellon University Robotics Institute PittsburghPA15213 United States

Inter-robot relative positions are crucial for executing various multirobot missions, such as formation maneuvering and collaborative inspection. However, the current sensing technology usually provides part of relative position information, such as inter-robot distances, bearings and angles. This prompts the study of determining inter-robot relative positions, i.e., relative localization, from these partial measurements. Based on the existing results of static networks' localizability and mobile robots' relative localization, we propose a novel concept, relative localizability to describe whether a multirobot system is relatively localizable. Given each robot's self-displacement measurements and inter-robot partial measurements in d (d ≤ 4) sampling instants, we show that a multirobot system's relative localization can be achieved in a purely algebraic and distributed manner, in which the multirobot system is said to be d-step relatively localizable. To make the results more general, we consider that the multirobot system consists of landmarks, leaders, and followers, and that the inter-robot measurements can be distances, bearings or angles. When robots' coordinate frames have different orientations, we show that the given local measurements can be used to determine robots' relative positions and their coordinate frames' relative orientations simultaneously. Simulations and experiments of relative localization for ground robots are conducted to validate the obtained results. © 2004-2012 IEEE.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

引用

5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Finite-Time Active Disturbance Rejection control for Electromagnetic Formation Flight in Near-Earth Orbit 2nd

Finite-Time Active Disturbance Rejection Control for Electr...

引用

2nd International Conference on the Frontiers of Robotics and Software engineering, FRSE 2024

作者： Shen, Xixi Meng, Bin Hu, Jiangping Zhou, Qingrui School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Control Engineering Beijing100080 China University of Electronic Science and Technology of China Huzhou313000 China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing100094 China

ISBN: (纸本)9789819631605

This paper investigates the position tracking control problem in electromagnetic satellite formation by utilizing the relative motion CW equation as the dynamic model. Firstly, a finite-time state feedback controller is derived using the adding a power integrator method, without considering external disturbances. This controller constitutes the feedback component of the composite controller. Subsequently, when the relative velocity of the system cannot be measured and is subject to external disturbances, a finite-time extended state observer is designed to estimate the relative velocity and disturbances of the system. The estimated disturbances serve as feedforward compensation, and the combination of the observer and the feedback control law achieves finite-time control without velocity measurements. Finally, it is proven that the proposed control law can achieve position tracking within a finite time. Numerical simulations at the end of the paper validate the theoretical analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disturbance rejection

来源：评论

学校读者我要写书评

暂无评论

A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning of Robotic Manipulation for Whip Targeting

Deep Reinforcement Learning of Robotic Manipulation for Whip...

引用

2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Bai, Xiang Wang, Junyi Xiong, Xiaofeng Boukas, Evangelos Technical University of Denmark Applied Mathematics and Computer Science Copenhagen Denmark Technical University of Denmark Automation and Control Group Electrical and Photonics Engineering Copenhagen Denmark University of Southern Denmark Sdu Biorobotics Mærsk Mc-Kinney Moller Institute Odense Denmark

ISBN: (纸本)9798331516857

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

GGIMN: Gravity Gradient Image Matching Network with Adaptive Adjoint Smoothing for AUV Navigation

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Li, Tianjiao Wang, Bo Song, Bao Deng, Zhihong Fu, Mengyin Beijing Institute of Technology School of Automation Beijing100081 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin300382 China Beijing Institute of Technology Engineering Research Center of the Ministry of Education for Navigation Guidance and Control Technology School of Automation Beijing100081 China Nanjing University of Science and Technology Nanjing210014 China

Gravity gradient-aided navigation is a promising navigation method for autonomous underwater vehicle (AUV), in which matching algorithm is its key technology. Image matching algorithms have higher matching accuracy and information utilization than single point matching and sequence matching. However, the spatial domain image feature matching ability is weak in areas with insignificant feature changes due to the uneven feature distribution in the gravity gradient reference map. To break through this limitation and solve the problem of insufficient spatial domain image feature matching ability, we propose a gravity gradient image matching network (GGIMN) with adaptive adjoint smoothing for AUV navigation and positioning. First, a training set for GGIMN with equal numbers of positive and negative samples is constructed by preparing the gravity gradient reference map and fitting the gravity gradient measured map. Then, the feature extraction part of MatchNet is expanded to construct a deep learning network with five gravity gradient twin towers in parallel. Subsequently, an adaptive adjoint smoothing method that combines the idea of sequence matching is proposed to solve the problem of unsmooth matching trajectory and further improve the matching accuracy. Finally, the GGIMN is trained offline, and the gravity gradient map is matched and positioned online. The results of marine experiments show that the GGIMN has higher matching and positioning accuracy, and the average positioning error is reduced beyond 70% compared with onedimensional matching algorithms such as single-point iteration and sequence matching. © 1967-2012 IEEE.

关键词： Image matching

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：