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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Terahertz time-of-flight tomography algorithm for different curvature samples

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Optics Express 2025年第7期33卷 15095-15109页

作者： Zhou, Yu Li, Quanyong Cui, Zongyu Zhang, Jiyang Ren, Jiaojiao Zhang, Dandan Gu, Jian Li, Haoxiang Lin, Zihao Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education Changchun University of Science and Technology Changchun130022 China School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute Changchun University of Science and Technology Zhongshan528403 China

Traditional guide rail-based terahertz reflection tomography works well with planar samples and complements transmission tomography. Most imaging methods perform effectively with plane-like or simple curvature samples. However, these methods are ineffective for complex shapes due to the challenges of dealing with internal corrections and stitching errors. To address the above issues, terahertz time-of-flight tomography is applied to the prepared samples with abnormal curvature using a rotating scanning array and "point cloud splicing" method. This approach enables comprehensive sample information collection and resolves the inner layer point cloud position correction challenge. A radial distance point cloud down-sampling method is proposed to reduce the impact of overlapping point clouds on reconstruction accuracy. After down-sampling, the outer layer reconstruction error reduces to 0.22%. Poisson surface reconstruction further enhances the intuitiveness of imaging results, and the reconstruction accuracy is 0.01 mm. This study broadens the application of terahertz time-of-flight tomography and offers a different approach for reflection tomography. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Surface reconstruction

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Synchronous Estimation Scheme of PMSM Mechanical Parameters: A Decoupled Extended Sliding Mode Observer

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IEEE Transactions on Power Electronics 2025年第8期40卷 10877-10888页

作者： Chang, He Wang, Zuo Yang, Jun Li, Shihua Lu, Shaowu Southeast University School of Automatin Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of CSE Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

In the permanent magnet synchronous motor drives (PMSM), high-quality controller optimization and condition monitoring rely heavily on accurate knowledge of mechanical parameters. To address this need, this study presents a novel decoupled extended sliding mode observer (DESMO) capable of achieving high-quality synchronous estimation of the moment of inertia and the load disturbance. An extended state-space equation is, firstly, constructed by extending the mechanical equations. Subsequently, the novel DESMO is designed by introducing two new recursive equations. On this basis, the excitation weight on the moment of inertia is analyzed, resulting in the derivation of Lyapunov-mechanism-based decoupling updating conditions (DOCs) to further exploit the DESMO's estimation potential. Furthermore, when the DOCs are not all satisfied, the second observation equation is derived with the reaching law of the moment of inertia set to zero to achieve state decoupling during estimation process. With the switching excitation of DOCs, notably, the DESMO is capable of serving as a more competitive candidate for observing multi-mechanical parameters, as demonstrated through simulation and experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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Lyapunov-based stochastic model predictive control of stochastic nonlinear systems with input-delay using multi-dimensional Taylor network

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Journal of the Franklin Institute 2025年第9期362卷

作者： Zheng, Xiao-Yi Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China School of Automation Southeast University Jiangsu Nanjing210096 China

Most of the existing stability control approaches to stochastic nonlinear systems with input-delay are based on robust mechanisms, which tend to be too conservative. Here, a Lyapunov-based stochastic model predictive control (LSMPC) scheme using the Fokker–Planck equation (FPE) is proposed to solve the problem. In the framework of the LSMPC approach, input-delay is compensated by the predictor feedback strategy, and the LSMPC method is extended to stochastic nonlinear systems with input-delay. FPE is introduced to describe the dynamic evolution of the probability density function (PDF) for stochastic nonlinear systems. The multi-dimensional Taylor network (MTN) is applied for approximating the solution of FPE. The proposed FPE-based LSMPC method proves to be capable of securing the probabilistic feasibility and stability of the closed-loop system. The closed-loop states turn out to succeed in converging to the small region around the origin in probability. The validity of the presented scheme is verified by an example. © 2025 The Franklin Institute

关键词： Predictive control systems

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Congruent-transformation-based stability criterion of T-S fuzzy systems with time-varying delay via the generalized line-integral Lyapunov function

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14669-14678页

作者： Liu, Zhou-Zhou Jin, Li He, Yong China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

This paper is concerned with the stability analysis of Takagi-Sugeno (T-S) fuzzy systems with time-varying delays. First, by constructing a generalized line-integral Lyapunov function (GLILF), applying the free-matrix-based inequality and the delay-derivative-dependent free-weighting-matrices method, a basic stability criterion is derived. Besides, the structure of the matrices in the stability criterion is reconstructed through congruent transformation, and an improved stability criterion is derived accordingly. To clarify the advancement of the proposed method, a stability condition is given without the GLILF. Finally, two numerical examples and a practical example are applied to show the advancement and merits of the presented method. © 2004-2012 IEEE.

关键词： Lyapunov functions

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Prediction of transmission spectrum from photonic energy band via autoencoder network

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IEEE Photonics Technology Letters 2025年第13期37卷 737-740页

作者： Mo, Wenqin Zhu, Yaling Yin, Yuanxing Dong, Kaifeng Jin, Fang Song, Junlei China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Optical topological insulators, as an emerging type of photonic material, present substantial benefits for optical communication. The advanced pattern recognition capabilities of deep learning have propelled the inverse design of topological photonic structures into a hot area of research. However, existing methods encounter difficulties with the analysis of intricate band structures. Therefore, we construct a deep learning model capable of handling two-dimensional data of photonic energy band. Utilizing an autoencoder network, our model indirectly trains the photonic energy band and transmission spectrum, by converting them into feature coding. It integrates a transmission spectrum autoencoder and a photonic energy band autoencoder, processing energy band data to predict the corresponding transmission spectrum. The results indicate that compared to convolutional neural network (CNN), our model can accurately predict transmission spectrum, and perform better on both evaluation metrics of RMSE and R2. This work enhances prediction accuracy by leveraging energy band diagrams to constrain optical properties. © 1989-2012 IEEE.

关键词： Topological insulators

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Moving-Window Integrated Physics-Data-Based Modeling of Four-Wheel Vehicle Lateral Dynamics

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Automotive Innovation 2025年 1-11页

作者： Wei, Wenpeng Wang, Jinxiang Pi, Dawei Zhuang, Weichao He, Tianyi Yin, Guodong School of Mechanical Engineering Southeast University JiangSu Nanjing 211189 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University JiangSu Nanjing 211189 China Safety and Intelligence of Vehicle Equipment Research Center Nanjing University of Science and Technology JiangSu Nanjing 210094 China Department of Mechanical and Aerospace Engineering Utah State University Logan 84322 UT United States

This paper presents an Integrated physics-data-based (IPDB) modeling of lateral vehicle dynamics with moving-window data snapshots. The IPDB model encodes the fundamental physical principle of four-wheel vehicle motions and simultaneously carries out the adaptiveness of the data-driven approach. Specifically, the traditional physics-based lateral dynamics considering four-wheel interaction are first derived into an affine linear-parameter-varying model, in which the vehicle-related parameters and motion variables are separated. Then, by using the Kronecker product, the IPDB model, directly formulated by the data snapshots in the moving-window fashion, is obtained for system representation. As a result, the IPDB technique rendered model is physically interpretable. The impacts of moving window length on modeling performance are numerically studied. The IPDB model accuracy is validated with data from CarSim simulations and experiments with passenger vehicles under various scenarios. It is further demonstrated that the proposed IPDB model is more data-efficient than other data-driven methods since it only uses the data snapshot in the moving window to update the model recursively. This characteristic enables the IPDB method with online modeling capability to adapt to varying driving scenarios. © The Author(s) 2025.

关键词： Autonomous vehicles Four-wheel vehicle dynamics Integrated physics-data-based modeling Linear parameter varying system

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BeltLineNet: A Shape-Prior-Guided Lightweight Network for Real-Time Deviation Detection in Circular Pipe Conveyors

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IEEE Sensors Journal 2025年第11期25卷 20024-20036页

作者： Zhao, Long Su, Jinhui Zhong, Yusheng Xie, Weiwei Su, Jinya Chen, Xisong Chen, Congyan Li, Shihua Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Fujian Longking Intelligent Conveying Engineering Co. Ltd Longyan China

Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring safe and efficient operation. However, existing methods, primarily developed for flat-belt systems, heavily rely on distinct belt-background semantics, making them unsuitable for circular conveyors where indistinct target features, motion blur, and occlusions pose significant challenges. To address these issues, we reformulate the deviation detection task as an object detection problem, proposing BeltLineNet, a lightweight, real-time deviation detection network specifically designed for circular pipe conveyors. Our method integrates explicit feature learning with a coarse-to-fine global feature fusion mechanism, enhancing belt line representation under complex conditions. Additionally, a shape-prior loss strategy is introduced to improve supervision during training, ensuring more accurate detection of elongated targets. The model is also optimized for real-time deployment through layer-adaptive pruning, achieving a balance between accuracy and computational efficiency. Extensive comparative experiments involving 8 state-of-the-art (SOTA) feature extraction networks and 6 feature enhancement strategies on a self-collected dataset under industrial conditions demonstrate that BeltLineNet surpasses SOTA feature extraction and fusion networks, improving average precision (AP) by 8.23% before pruning and 6.33% after pruning. © 2001-2012 IEEE.

关键词： Belt conveyors

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Adaptive Cooperative Estimation and Guidance Against Unknown Maneuvering Target

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Tianrun Chen, Yang-Yang Li, Shihua Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Advanced Ocean Institute of Southeast University Nantong226000 China

A critical challenge in missile guidance is the frequent inaccessibility of essential target information by on-board seekers. This paper studies the three-dimensional cooperative guidance issue of multiple missiles simultaneously attacking an unknown maneuvering target. Different from most existing research, the pitch and yaw lead angles of a maneuvering target are entirely unknown, while the position and velocity information is only accessible to neighboring missiles. An adaptive distributed estimator is designed to estimate the target information over a directed communication network, generating a transmitted target with similar maneuverability to the actual target for each missile. Boundedness of the estimation errors are guaranteed within a prescribed time. Subsequently, a modified guidance law that integrates proportional navigation (PN) and coordination components is developed for simultaneously tracking the trajectories of the transmitted targets. The superiority of the developed adaptive cooperative estimation and guidance approach is confirmed through simulations. © 1965-2011 IEEE.

关键词： adaptive estimate Cooperative guidance maneuvering target prescribed-time three-dimensional

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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