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Physical Layer Security Scheme With AoI-Awareness for industrial IoT Based on Covert Communications

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 276-278页

作者： Yaping Li Zhi-Xin Liu Jia-Wei Su Ya-Zhou Yuan the Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Dear Editor,industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].

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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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Dynamic Event-Triggered Networked Adaptive Tracking control of Wind Turbine Systems

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14371-14382页

作者： Chen, Jun Meng, Wenchao Gong, Yingjie Yang, Qinmin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310000 China

This paper addresses the co-design problem of adaptive control for networked wind turbine systems that track the desired rotor speed while efficiently scheduling network communication. Unlike existing approaches, the proposed method integrates the control design and the communication considerations, ensuring asymptotic tracking of rotor speed under the generator torque saturation with significantly reduced communication load. Firstly, the communication scheme is developed using the dynamic event-triggering mechanism, which introduces the feedback signal in the sampling loop. Secondly, an auxiliary signal is designed to mitigate the negative effect of inevitable generator torque saturation, ensuring that the bounded controller asymptotically exits from saturation. Then, the adaptive torque controller is constructed with the compensation signals to guarantee asymptotic stability, and the measurement function for dynamic event-triggering is co-designed alongside the controller to regulate the sampling-induced error. Furthermore, the requirement for exact knowledge of the wind turbine systems is eliminated by utilizing an online approximator to learn the uncertain aerodynamics and parameters. Finally, it is theoretically proven that all the signals in the closed-loop system are bounded, and the rotor speed asymptotically tracks the reference rotor speed. The feasibility and advantages of the proposed method are demonstrated on the NREL 5-MW wind turbine using the high-fidelity OpenFAST simulation platform. © 2004-2012 IEEE.

关键词： Networked control systems

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Toward Universal controller: Performance-Aware Self-Optimizing Reinforcement Learning for Discrete-Time Systems With Uncontrollable Factors

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3249-3260页

作者： Zhang, Jianfeng Zhang, Haoran Zhao, Chunhui Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

The industrial system usually contains not only controllable variables (CVs) but also uncontrollable variables (unCVs), e.g., weather conditions and friction. These unCVs have a direct impact on system control performance. Despite the success of current deep reinforcement learning (DRL) control algorithms, most of them neglect the impact of unCVs, which can cause the deterioration of control performance and instability of the system. To perceive and eliminate the impact of unCVs, a performance-aware self-optimizing universal controller (PASOUC) is designed in this article. The PASOUC aims at integrating the representation of unCVs and controller design to perceive and eliminate the impact of unCVs under different conditions, which goes beyond most existing control methods. Technically, a historical trajectory-inspired control performance perceptron is developed to perceive the impact of unCVs on system control performance under different conditions. Subsequently, a new performance-aware reward is designed to integrate the representation of unCVs and controller design while training the DRL controller. In addition, the domain randomization (DR) training strategy is employed to learn a universal control policy, which can access the approximate optimal trajectory under nonideal conditions. In this way, the impact of unCVs can be eliminated. To handle the low efficiency of the DR training, the policy improvement-policy proximal optimization (PI-PPO) is proposed to enhance the convergence speed of the DR training by performing explicit policy improvement. Finally, illustrative examples are presented to demonstrate the superiority of the proposed method. © 2013 IEEE.

关键词： Deep reinforcement learning

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Multi-Task Load Identification and Signal Denoising via Hierarchical Knowledge Distillation

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1967-1980页

作者： Jiang, Jiahao Wang, Zhelong Qiu, Sen Li, Xiang Zhang, Chenming Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education School of Control Science and Engineering Dalian116024 China

Complex neural networks with deep structures are beneficial for solving problems such as load classification in Non-intrusive load monitoring (NILM) due to their powerful feature extraction capabilities. Unfortunately, corresponding complex models designed based on deep learning algorithms require high computational and memory resources. Additionally, the external noise interference during practical load identification poses a challenge. To solve these difficulties with practical industrial significance, this paper proposes a multi-task-knowledge distillation (MTL-KD) framework for NILM. The main contributions within this framework include a new feature extraction method that combines variational mode extraction (VME) and mutual information (MI) to extract unique features and filter out noise interference, an attention-based MTL model to simultaneously perform the load identification and signal de-noising tasks, and new KD modules to transfer knowledge from a complex teacher model to a small student model. Experimental evaluations conducted on public datasets such as the plug-load appliance identification dataset (PLAID) and the worldwide household and industry transient energy dataset (WHITED), as well as a private load dataset collected in the lab, demonstrate that the proposed MTL-KD framework surpasses state-of-the-art approaches. © 2013 IEEE.

关键词： Signal denoising

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Physically informed hierarchical learning based soft sensing for aero-engine health management unit

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Chinese Journal of Aeronautics 2025年第3期38卷 374-385页

作者： Aina WANG Pan QIN Yunbo YUAN Guang ZHAO Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian 116024China School of Energy and Power Engineering Dalian University of TechnologyDalian 116024China

Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management *** existing Physics-Informed Neural Networks(PINNs)have made ***,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs *** this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is ***,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are *** is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU ***,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss ***,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed ***,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’***,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft ***,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.

关键词： Hierarchical learning strategy Monitoring:Partial differen tial equations with unmeasurable driving terms Physically informed hierarchical learning followed by recurrent-prediction term Soft sensing

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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CF6D: A Visual Perception Network for Collision-Free Grasping and Manipulation in Complex industrial Environments

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IEEE Sensors Journal 2025年第9期25卷 16250-16259页

作者： Li, Cuiming Shi, Lei Shen, Tao Kang, Ru Lanzhou University of Technology School of Mechanical and Electrical Engineering National Key Laboratory of Large Electrical Drive Systems and Equipment Technology Key Laboratory of Advanced Control of Industrial Processes of Gansu Province Lanzhou730050 China

Accurate 6-D pose estimation is critical for robots to perform grasping tasks successfully. In recent years, numerous 6-D pose estimation algorithms have been widely applied in robotic grasping experiments. However, in complex environments, industrial robots often struggle to extract effective image features, leading to inaccurate 6-D pose estimations and subsequent grasping failures. In addition, improper grasping sequences caused by stacked parts increase the risk of collisions, further elevating the probability of grasping failure. To overcome these challenges, we presented CF6D, a collision-free grasping network based on 6-D pose estimation. Specifically, we devised a keypoint prediction network that utilizes a least squares fitting algorithm to exactly detect object keypoints and estimate 6-D poses. The network also outputs an optimal grasping sequence, enabling the robot to effectively grasp stacked parts based on precise poses and a reasonable order. On the relevant dataset, our method is obviously better than the existing methods in terms of accuracy and robustness. Furthermore, test results on a real-world robotic platform demonstrate that this method excels in practical grasping tasks, meeting the demands of industrial applications. © 2025 IEEE.

关键词： industrial robots

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Dynamic Modeling and control for an Offshore Semisubmersible Floating Wind Turbine

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12371-12382页

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Automation Beijing100084 China Zhejiang Windey Company Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners - This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based n

关键词： Renewable energy

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