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Stability and Stabilization of Sampled-Data Based LFC for Power systems:A Data-Driven Method

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

关键词： Sampled data control systems

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Noncontact Video-Based Vital Signs Continuous Monitor for Unconstrained Conscious Rodents

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IEEE Sensors Journal 2025年第10期25卷 16628-16639页

作者： He, Yuke Gao, Tong Lv, Yong Zhang, Jingjing 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 Wuhan430074 China

Animal experiments should minimize pain and discomfort and maximize animal welfare to satisfy the experimental animal 3R principle. The quality of vital signs monitor results collected from conscious and stress-free rodents is superior, ensuring experimental data’s reliability, accuracy, and scientificity. This work proposes a noncontact video-based vital signs continuous monitoring method for unconstrained conscious rodents. First, rodents are tracked using keypoint detection, and regions of interest (ROI) on the rodents are selected based on signal-to-noise ratio (SNR) distribution of vital signs. Second, corner motion within the ROI region is tracked to synthesize skin pulsations, from which respiratory signals are extracted. Finally, a method is proposed to eliminate the influence of respiratory harmonics, thereby removing respiratory interference and extracting the heart rate (HR). Four rats and four mice were used to measure the performance of the proposed method. This work is the first to use a camera to continuously monitor the HR of unrestrained and conscious rodents. Compared with the ground truth [semi-implantable electrocardiogram (ECG)], the relative HR error was 1.86% \pm ~0.82 % and 1.20% \pm ~0.33 % for rats and mice, respectively. This method is used for unconstrained conscious rodents and has high applicability, allowing it to be used in various experimental environments and conditions. At the same time, this work contributes to the development of new methods for continuously and objectively evaluating animal welfare, thereby promoting the development of modern biomedical and ethical research. © 2001-2012 IEEE.

关键词： Invertebrates

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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Improving Multi-task GNNs for Molecular Property Prediction via Missing Label Imputation

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Machine Intelligence Research 2025年第1期22卷 131-144页

作者： Fenyu Hu Dingshuo Chen Qiang Liu Shu Wu University of Chinese Academy of Sciences Beijing100190China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of AutomationChinese Academy of SciencesBeijing100190China

The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there is a great need to develop the multi-task learning ability of ***,limited by expensive and time-consuming human annotations,collecting complete labels for each task is *** a result,most existing benchmarks involve many missing labels in training data,and the performance of GNNs is impaired due to the lack of sufficient supervision *** overcome this obstacle,we propose to improve multi-task molecular property prediction by missing label ***,a bipartite graph is first introduced to model the molecule-task co-occurrence ***,the imputation of missing labels is transformed into predicting missing edges on this bipartite *** predict the missing edges,a graph neural network is devised,which can learn the complex molecule-task co-occurrence *** that,we select reliable pseudo labels according to the uncertainty of the prediction *** with enough and reliable supervision information,our approach achieves state-of-the-art performance on a variety of real-world datasets.

关键词： Graph classification imbalance learning prediction bias mixture of experts multiview representations

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Time-varying formation-containment control for heterogeneous multi-agent systems with communication and output delays via observer-based feedback protocol

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15435-15448页

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

In this article, the time-varying formation-containment control problem of heterogeneous multi-agent systems (MASs) with an observer-based state feedback protocol is studied, where both communication delays and output delays are taken into consideration. By introducing an augmented Lyapunov-Krasovskii functionals (LKFs) candidate and employing less conservative methods to estimate its derivative, an improved quasi-formation-containment criterion is derived. On this basis, some observers and controllers are designed to allow for a larger maximum upper bound (MUB) of time delays on the premise of guaranteeing the fulfillment of the time-varying formation of leaders and the bounded convergence of followers. The estimation of the MUB determines the allowable delay range for system stability under the designed observers and controllers. Moreover, a distributed algorithm is developed to calculate the MUB of time delays, the observer gains, and the controller gains. Finally, two simulation experiments are implemented to demonstrate the obtained results, which verify the superiority and practicability of the criterion and algorithm. © 2004-2012 IEEE.

关键词： State feedback

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Energy-based Swing-up Control of the Soft Inverted Pendulum with Affine Curvature

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Nonlinear Dynamics 2025年 1-20页

作者： Wang, Yongjia Xin, Xin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Institute of Intelligent Unmanned Systems Southeast University Nanjing210096 China

This paper studies the energy-based swing-up control of an underactuated soft inverted pendulum, a template model of soft robots with its base attached to the ground and its curvature described by an affine function. First, this paper presents a necessary and sufficient condition for the linear controllability of the robot around its upright equilibrium point (UEP). Next, without any constraint on its stiffness, this paper derives an energy-based controller to swing up the robot to its UEP, where the pendulum is in its upright position, and provides a necessary and sufficient condition such that the controller is free of singular points. Then, this paper characterizes the patterns of closed-loop equilibrium points with respect to the physical parameters of the pendulum and the gains of the derived controller, and analyzes the closed-loop motion corresponding to the special case in which desired convergence of the total mechanical energy is achieved. In such case, this paper reveals that the UEP is generically the unique closed-loop equilibrium point, and provides clarification regarding its stability. These results serve as guidelines of the design of soft robots and the tuning of control gains to achieve the desired performance of the swing-up control. Finally, this paper conducts numerical simulations to validate the theoretical results and to demonstrate the effectiveness and performance of the derived controller in swinging up the robot to its UEP from initial states far from the UEP, even under the challenging condition of very low stiffness. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Affine transforms

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A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. This incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © IFIP International Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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Deep Reinforcement Learning-Assisted Robust Cubature Kalman Filter for Power System Dynamic State Estimation With Multi-Rate Measurements

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14346-14357页

作者： Gu, Haoli Liu, Shichao Chen, Bo Wang, Rusheng Yu, Li Kaynak, Okyay Zhejiang University of Technology Department of Automation Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China Ningbo University of Technology Ningbo315201 China Bogazici University Istanbul34342 Turkey

The coexistence of high-frequency phasor measurement units (PMUs) and conventional SCADA systems raises the challenge of heterogenous-source and multi-rate measurements which significantly degrades the performance of power system dynamic state estimation. In this work, a deep reinforcement learning (DRL) assisted robust cubature Kalman filtering (CKF) scheme is proposed to handle measurements from hybrid sources and with different time scales. In specific, a multi-rate measurement function reconstruction approach is designed with an independent discretization mechanism to lift the present limitation of requiring an integer multiple relationship of the sampling rates from multiple sources in most of existing works. Embedded with this discretization mechanism, a deep reinforcement learning assisted two-parameter linear exponential smoothing method is proposed to reconstruct the slow measurement model with online adjustable estimation parameters. A generalized correntropy loss criterion is also included in the robust CKF to counter the non-Gaussian noise and the noise distribution variation caused by the reconstruction. Comparisons results demonstrate that the proposed DRL-based robust CKF method can achieve better accuracy and robustness under various operating scenarios. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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Surface-Continuous Scene Representation for Light Field Depth Estimation via Planarity Prior

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 5051-5066页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cui, Zhenglong Cong, Ruixuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Zhejiang Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences China

Light field (LF) imaging captures both spatial and angular information of the real world, enabling precise depth estimation. However, images are merely discrete expressions of scenes. Limited by imaging technology, LF camera cannot capture the infinite rays emitted by scenes, leading to the discrete information storage (e.g. pixel). Consequently, previous deep learning methods have encountered challenges in accurately extracting depth information from LF images. In this paper, we investigate a surface-continuous scene representation using planarity prior and design PlaneNet, a Plane-based Network that successfully generates highly detailed depth maps for real scenes. Specifically, inspired by the plane assumption that real-world scenes generally yield piecewise smooth surfaces, we refine it to the pixel level for continuous surface approximation, which can overcome the limitations of discrete representation. Rather than explicitly parameterizing planes as multiple coefficients, we propose a novel plane regular sampling operator (PRSO), enabling the network to fit smooth depth surfaces easily. To explore the role of our theory at the feature level, we also introduce PRSO into the intermediate layers of PlaneNet. Experiments show that our method achieves state-of-the-art performance on both synthetic and real-world LF scenes, ranking 1st (MSE) on the HCI 4D Light Field benchmark. Furthermore, we explore the utilization of our representation in multiple LF depth estimation networks, and experiments demonstrate improved performance when surface-continuous representation is applied. © 1991-2012 IEEE.

关键词： Pixels

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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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