检索结果-内蒙古大学图书馆

PMSM Speed Ripple Suppression Due to Current Measurement Error Using Quasi-Fractional Resonant-Normalized Extended State Observer

引用

IEEE TRANSACTIONS ON control systems TECHNOLOGY 2025年第2期33卷 554-565页

作者： Chen, Pengchong Luo, Ying Zhang, Li Wang, Xiaohong Chen, Yangquan Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Inst Robot Zhengzhou 450001 Peoples R China South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510610 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Univ Calif Merced Sch Engn Dept Mech Engn Merced CA 95343 USA

Current measurement error causes periodic speed ripple in permanent-magnet synchronous motor control systems. The typical normalized extended state observer (NESO) can estimate and compensate for the load disturbance but is not sufficiently capable to deal with the periodic disturbances. Thus, a speed controller based on the NESO frame, combining a PD controller and a quasi-fractional-resonant (QFR) controller, is proposed to address this speed ripple issue due to the current measurement error including offset error and scaling error. Then, it is verified that the proposed controller satisfies the separation principle and QFR controller does not affect the open-loop characteristics of the system through mathematical derivation in the frequency domain. Besides, the proposed controller in outer speed loop and a PI controller in inner current loop are designed with the proposed analytical parameters' tuning methods based on frequency-domain analysis. Compared with the existing adaptive proportional-integral-resonant (PIR) controller, the proposed controller not only suppresses the speed ripple to a lower level but also achieves better speed tracking and load disturbance rejection performances. The experimental result comparison confirms the effectiveness of the proposed controller and design scheme.

关键词： Current measurement control systems Measurement uncertainty Torque Stators State estimation PD control Synchronous motors Resistance PI control Current measurement error fractional-resonant (FR) controller normalized extended state observer (NESO) permanent-magnet synchronous motor (PMSM) speed ripple

来源：评论

学校读者我要写书评

暂无评论

Towards training noise-robust anomaly detection via collaborative adversarial flows

引用

MEASUREMENT 2025年 242卷

作者： Cheng, Hao Luo, Jiaxiang Zhang, Xianyong Liu, Haiming Wu, Fan South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Minist Educ Precis Elect Mfg Equipment Engn Res Ctr Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Guangdong Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou 510180 Guangdong Peoples R China Guangdong MOJE Intelligent Equipment CO Ltd Guangzhou 510765 Guangdong Peoples R China

Unsupervised visual anomaly detection (UVAD) aims at identifying unobserved anomalies by training a classification model with anomaly-free images, which has been widespread applied to surface inspection of multiple industrial products. However, previous UVAD methods trained by ideal normal samples suffer from performance degradation in real world industry because of the inevitable intrusion of noisy samples into the training set. Addressing this issue, this article introduces a novel strategy, Collaborative Adversarial Flows (CAF), which employs multiple normalizing flows to efficiently filter out noisy samples and accurately construct distributions of normal samples. Unlike previous methods that focused solely on learning accurate representations of normal samples, CAF adds an additional learning objective of inferring noisy samples based on distributional metrics while optimizing representations. Furthermore, CAF establishes a flow-based collaborative adversarial paradigm that promotes high-likelihood samples and rejects low-likelihood noisy samples in optimizing the latent distribution, thereby enhancing the robustness of model under contaminated training set. Comprehensive experiments on the public benchmark MVTec AD under human-made noise intrusion demonstrate that CAF effectively improves the robustness to noisy samples and achieves impressive performance. In electronic manufacturing processes with real industrial noise, the applicability and robustness of CAF have been further proved by the inspection of surface mount devices.

关键词： Noise-robust anomaly detection Real-world defect inspection Training noise Collaborative adversarial optimization Normalizing flows

来源：评论

学校读者我要写书评

暂无评论

CT-NeRF: Incremental Optimization of Neural Radiance Field and Camera Poses with Complex Trajectory

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Ran, Yunlong Li, Yanxu Ye, Qi Huo, Yuchi Cui, Zhaopeng Bai, Zechun Sun, Jiahao Chen, Jiming Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University College of Control Science and Engineering State Key Laboratory of Industrial Control Technology China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Zhejiang University State Key Lab of CAD and CG China

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction and optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojection error constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRF-Buster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy. © 1991-2012 IEEE.

关键词： 3D Reconstruction bundle adjustment NeRF novel view synthesis structure from motion

来源：评论

学校读者我要写书评

暂无评论

Interaction between vortex shedding and flap oscillation in separation control of a stalled aerofoil

引用

Aerospace Science and Technology 2025年 163卷

作者： Yang, Yannian Li, Pengyu Zhou, Yongsheng Zhou, Tao Liu, Yu Pröbsting, Stefan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China

Flow control for lifting surfaces at high angle of attack is essential to increase the lift coefficient. Inspired by covert feathers of birds, a passive rotatable flap is added to the suction surface of an aerofoil to analyze its effectiveness and flow control mechanism from the perspective of fluid structure interaction. The lift coefficients of the aerofoil are measured by a balance, increasing near the stall angle and reaching maximum augmentation of 25% with the flap. Particle image velocimetry measurements show that the transition of trailing edge shear layer to vortex shedding occurs later due to the flap, and the separation zone becomes smaller, which work together to enhance the lift-to-drag ratio. Flap movement is influenced by unsteady trailing edge vortex shedding, with the flap pop-up angle increasing together with the aerofoil angle of attack and reaching a maximum value around 84∘, beyond which it decreases again. Moreover, the experimental data observes the low frequency flow pulsation and flap oscillation in addition to the high frequency trailing edge vortex shedding, which has a modulation effect on the flow fluctuation and flap oscillation magnitude. © 2025 Elsevier Masson SAS

关键词： Vortex shedding

来源：评论

学校读者我要写书评

暂无评论

Robust Offline Imitation Learning Through State-level Trajectory Stitching

arXiv

引用

arXiv 2025年

作者： Wang, Shuze Mei, Yunpeng Cao, Hongjie Yuan, Yetian Wang, Gang Sun, Jian Chen, Jie National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

Imitation learning (IL) has proven effective for enabling robots to acquire visuomotor skills through expert demonstrations. However, traditional IL methods are limited by their reliance on high-quality, often scarce, expert data, and suffer from covariate shift. To address these challenges, recent advances in offline IL have incorporated suboptimal, unlabeled datasets into the training. In this paper, we propose a novel approach to enhance policy learning from mixed-quality offline datasets by leveraging task-relevant trajectory fragments and rich environmental dynamics. Specifically, we introduce a state-based search framework that stitches state-action pairs from imperfect demonstrations, generating more diverse and informative training trajectories. Experimental results on standard IL benchmarks and real-world robotic tasks showcase that our proposed method significantly improves both generalization and performance. Videos are available on https://***/sbr. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

引用

IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. A demonstration video is available at https://***/playlist?list=PL7et4nGJAImLGytsJbglGbXl1hacA2dy. © 1999-2012 IEEE.

关键词： Robot vision

来源：评论

学校读者我要写书评

暂无评论

Practical Prescribed Time control Framework for Decentralized Robust Steering of Connected Automated Vehicles Under Deception Attacks

引用

IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年

作者： Qi, Xuelei Li, Chen Ni, Wei Sheng, Quan Z. Ma, Hongjun Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Macquarie Univ Sch Comp Sydney NSW 2109 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia CSIRO Data61 Marsfield NSW 2122 Australia South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Minist Educ Guangzhou 510641 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China

Effective vehicle control contributes to the safety and efficiency of connected automated vehicles (CAVs). Many existing solutions do not consider the maximum effective communication distance and bearing angle constraints between vehicles. This article proposes a novel prescribed performance method to handle distance and angle constraints to achieve vehicle stability under deception attacks. A key aspect is that the above two constraints are successfully transformed from inequality-constrained form to equivalent equation unconstrained form through introducing error transformations, and we prove that the errors of distance and angle are strictly contained within the boundary of the performance function. Another key aspect is to use adaptive bias radial basis function neural network (RBFNN) to approximate unknown nonlinear functions and deception attacks in the system and integrate the approximated results into recursive construction to design adaptive laws and multilane merging control laws. Analysis shows that all signals in a closed-loop system are practical prescribed time stable. Simulations validate that our control method has a faster convergence time than the existing advanced two-dimensional (2-D) vehicle approach and can adaptively adjust convergence to predefined sets under different attack intensities.

关键词： Merging Vehicle dynamics Safety Artificial neural networks Cyberattack Australia Resistance Radial basis function networks Nickel Mathematical models Adaptive bias radial basis function neural network (RBFNN) connected automated vehicle (CAV) cooperative lane-merging control deception attack practical prescribed time control RBFNN

来源：评论

学校读者我要写书评

暂无评论

Object tracking based on particle filter with discriminative features

引用

控制理论与应用（英文版） 2013年第1期11卷 42-53页

作者： Yunji ZHAO Hailong PEI Key Laboratory of Autonomous Systems and Networked Control (Ministry of Education) South China University of Technology

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.

关键词： Histogram of oriented gradients Local discrimination Particle filter Multiple object tracking

来源：评论

学校读者我要写书评

暂无评论

Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

引用

IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Static anti-windup synthesis for linear systems subject to actuator amplitude and rate saturation using an LMI approach

Static anti-windup synthesis for linear systems subject to a...

引用

第三十二届中国控制会议

作者： WANG Naizhou PEI Hailong WANG Jun Key Laboratory of Autonomous Systems and Networked Control Ministry of Education in South China University of Technology

ISBN: (纸本)9781479900305

In this paper,a static anti-windup synthesis problem is *** original system with amplitude and rate saturation is simplified to the synthesis of a system with only amplitnde *** on the general sector condition, the static anti-windup synthesis is solved by linear matrix inequality(LMI) using Lyapunov's second *** with other approaches,the designed controller need not to be strictly proper and can be applied to both stable and unstable system. Meanwhile,the best performance index can be obtained by solving a convex optimization *** the end of the paper,a simulation example is shown to illustrate the effectiveness of the proposed approaches.

关键词： amplitude and rate saturation static anti-windup compensation linear matrix inequality

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：