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Noncollocated Output Feedback control for the Semilinear Parabolic PDEs Under Input and Output Quantization

IEEE Transactions on Automatic control 2025年第5期70卷 2934-2947页

作者： Zhao, Xuena Liu, Zhijie Zhang, Shuang Liu, Yu He, Wei University of Science and Technology Beijing Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Science and Engineering Engineering Research Center of Precision Electronic Manufacturing Equipment of Ministry of Education Guangzhou510640 China Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Beijing102206 China

In the context of increasingly networked control systems, this article concentrates on the noncollocated control issue of input and output quantization for industrial processes governed by semilinear parabolic partial differential equation (PDE) systems. Both the control and measurement signals are quantized and transmitted through the network, which causes signal errors and reduces the stability and robustness of the system. A Luenberger-type PDE observer is developed to overcome the control difficulty of quantized measurement feedback signals and noncollocated pointwise sensors. Based on the observed value, a feedback controller is proposed, which undergoes quantization and operates on the distributed actuators within the domain. By employing the Lyapunov direct method, it is demonstrated that the observer can accurately track the system state with exponential precision, and the closed-loop system is exponentially converged. The well-posedness analysis of the closed-loop system is discussed via the C0-semigroup method. The performance of the developed control strategy is validated through simulation examples. © 1963-2012 IEEE.

关键词： Feedback control

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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...

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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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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi 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

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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Building intelligence identification system via large language model watermarking: a survey and beyond

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Artificial Intelligence Review 2025年第8期58卷 1-58页

作者： Wang, Xuhong Jiang, Haoyu Yu, Yi Yu, Jingru Lin, Yilun Yi, Ping Wang, Yingchun Qiao, Yu Li, Li Wang, Fei-Yue Shanghai Artificial Intelligence Laboratory Shanghai200433 China School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Automation BNRist Tsinghua University Beijing100084 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse. To mitigate these concerns, robust identification mechanisms are widely acknowledged as an effective strategy. Identification systems for LLMs now rely heavily on watermarking technology to manage and protect intellectual property and ensure data security. However, previous studies have primarily concentrated on the basic principles of algorithms and lacked a comprehensive analysis of watermarking theory and practice from the perspective of intelligent identification. To bridge this gap, firstly, we explore how a robust identity recognition system can be effectively implemented and managed within LLMs by various participants using watermarking technology. Secondly, we propose a mathematical framework based on mutual information theory, which systematizes the identification process to achieve more precise and customized watermarking. Additionally, we present a comprehensive evaluation of performance metrics for LLM watermarking, reflecting participant preferences and advancing discussions on its identification applications. Lastly, we outline the existing challenges in current watermarking technologies and theoretical frameworks, and provide directional guidance to address these challenges. Our systematic classification and detailed exposition aim to enhance the comparison and evaluation of various methods, fostering further research and development toward a transparent, secure, and equitable LLM ecosystem. © The Author(s) 2025.

关键词： Natural language processing systems

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Design and development of a new autonomous transportation robot for finished vehicles docking transportation in RO/RO logistics terminal

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Advanced Engineering Informatics 2025年 66卷

作者： Xu, Yongkang Zhang, Lin Liu, Zhi Wang, Shoukun Wang, Junzheng State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China

With the continuous growth of the automobile trade, the inefficiency of traditional cargo transshipment in Roll-On/Roll-Off (RO/RO) terminals has become increasingly pronounced. As a result, the adoption of autonomous transportation robot (ATR) for the automatic handling of finished vehicles has seen significant growth. However, ATRs designed for this purpose face several limitations, including suboptimal mobility performance and the necessity for additional infrastructure to support their operation. This paper introduces a novel ATR that offers enhanced flexibility and operational capability. To further optimize the positioning of LiDAR, we develop a multi-stage LiDAR fusion algorithm for the precise localization of finished vehicles, incorporating an event-triggered decision-making approach to improve positioning accuracy. Based on the accurate positioning data, we propose a docking strategy consisting of two key phases: the approach phase and the docking phase. During the docking phase, an enhanced Model Predictive control (MPC) algorithm, integrated with a Radial Basis Function (RBF) neural network, is designed to enable real-time adjustment of the robot's docking attitude. The effectiveness of the proposed approach is validated through real-world robot experimentals demonstrating its practical viability. © 2025

关键词： Docking control Finished vehicles RO/RO terminal Transportation robot

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l2 cluster synchronization for discrete-time Neural Networks with intermittent control

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Neurocomputing 2025年 623卷

作者： Xiao, Zijing Li, Zitian Zhou, Yumei Zhao, Yao Zhang, Tianyang Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou510006 China

This work addresses the cluster synchronization for discrete-time neural networks over energy-constrained communication channels subjecting to packet loss. A new intermittent control strategy including packet loss is employed, which not only reduces communication load but also save control energy. Additionally, the sufficient condition to achieve cluster synchronization is obtained by constructing interval-dependent function, and the l2-gain performance of the synchronization error is ensured to reduce the impact of unexpected exogenous perturbations via the intermittent control. Finally, the effectiveness and demonstrated of the results are illustrated by a numerical example. © 2025 Elsevier B.V.

关键词： Packet loss

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Design of a Constant Current Output High Frequency AC-DC Converter for Battery Charging Applications 26

Design of a Constant Current Output High Frequency AC-DC Con...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wei, Zihao Song, Xiaoxian Jin, Liangwei Huang, Dingbang Zhang, Sheng Fang, Zhijian School of Future Technology China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

How to realize the high power factor, high efficiency, miniaturization and high power density of AC-DC converter is the key problem of battery charging applications. In this paper, an isolated AC-DC converter is proposed. The input voltage is rectified by the first stage AC-DC conversion, and the output is stabilized by the series resonant converter at the secondary stage. A sinusoidal modulation method is adopted to realize the constant current output characteristic independent of load. Then through the output current control loop and the PFC control loop, the constant current output and input high power factor are realized. The design of high switching frequency and resonant frequency provides conditions for high power density and miniaturization of converter design. Finally, the simulation of rated power of 960W was built by Matlab-simulink, which verified the rationality of the converter design. © 2025 IEEE.

关键词： State of charge

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