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High-stability temperature control and frequency-domain analysis of sandwich-like insulation design based on phase change materials for satellite thermal management

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Science China Technological Sciences 2024年第8期67卷 2387-2404页

作者： WANG ZiHan HE ChenBo HU Yang TANG GuiHua Key laboratory of Thermo-Fluid Science and engineering Ministry of Education School of Energy and Power Engineering Xi'an Jiaotong University

High-stability thermal management is critical for the measurements of high sensitivity for temperature, but also challenging because any small thermal disturbances could lead to unacceptable temperature fluctuations. The present work delivers a design for passive temperature control, customized for a component in the satellites for gravitational wave detection. A novel sandwichlike structure is proposed with the configurations of proper materials, consisting of a layer of insulation material and two layers of nanocomposite phase change materials, bringing an integration of heat insulation and absorption/storage. Its performance is examined using an improved thermal network model and the revised transfer function method（TFM）. The basic results of the two methods are validated by present COMSOL simulations and available numerical and experimental data in the literature. An effective reduction of temperature fluctuation is achieved to the scale of 0.1 K, even under two thermal disturbances from different directions: a radiative heat flux of 20 W m-2（inside） and a temperature fluctuation of about 20 K（outside）. Moreover,the TFM is employed to analyze the effects of the frequency of thermal disturbance: excellent damping performance is obtained for over 3.2 mHz and the underlying mechanism is discussed. Overall, the present design is expected to be combined with active temperature control to explore more possible ways for temperature control with higher stability.

关键词： temperature control sandwich insulation structure phase change material thermal network model transfer function method

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Prescribed Performance control of One-DOF Link Manipulator With Uncertainties and Input Saturation Constraint

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 148-157页

作者： Yang Yang Jie Tan Dong Yue IEEE the College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications the Institute of Advanced Technology and jiangsu engineering laboratory of big data analysis and control for active distribution network Nanjing University of Posts and Telecommunications

In this paper, we mainly address the position control problem for one-degree of freedom(DOF) link manipulator despite uncertainties and the input saturation via the backstepping technique, active disturbance rejection control(ADRC) as well as predefined tracking performance functions. The extended state observer(ESO) is employed to compensate uncertain dynamics and disturbances, and it does not rely on the accurate model of systems. The tracking differentiator(TD) is utilized to substitute the derivative of the virtual control signals, and the explosion of complexity caused by repeated differentiations of nonlinear functions is removed. The auxiliary system is used to deal with the control input limitation, and the tracking accuracy and speed are improved by predefined tracking performance *** the help of the input-to-state stability(ISS) and Lyapunov stability theories, it is proven that the tracking error can be gradually converged into arbitrarily small neighborhood of the origin, and the tracking error is adjusted by suitable choice of control parameters. The simulation results are presented for the verification of the theoretical claims.

关键词： active disturbance rejection control (ADRC) auxiliary system backstepping technique input saturation predefined tracking performance function

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Event-trigger-based consensus secure control of linear multi-agent systems under DoS attacks over multiple transmission channels

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Science China(Information Sciences) 2020年第5期63卷 105-118页

作者： Yang YANG Yanfei LI Dong YUE College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications jiangsu engineering laboratory of big data analysis and control for active distribution network Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing University of Posts and Telecommunications

This paper proposes a consensus secure control scheme in the presence of denial-of-service(DoS)attacks based on an event-trigger mechanism. In contrast to a scenario in which attacks are the same and simultaneously paralyze all channels, the DoS attack addressed in this paper occurs aperiodically and results in the independent interruption of multiple transmission channels. A sufficient condition for the attack duration is designed and a distributed event-triggered control scheme is proposed, where the updated instants are triggered aperiodically to reduce the required communication resources. It is shown that the overall system is stable with the proposed scheme according to the Lyapunov stability theory and that Zeno behavior is excluded. Finally, a numerical example is presented to verify the effectiveness of the proposed scheme.

关键词： multi-agent system event-triggered control consensus secure control denial-of-service attack multiple transmission channels

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Distributed Tracking control of a Class of Multi-agent Systems in Non-affine Pure-feedback Form Under a Directed Topology

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 169-180页

作者： Yang Yang Dong Yue College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Advanced Technology and jiangsu engineering laboratory of big data analysis and control for active distribution network Nanjing University of Posts and Telecommunications Nanjing 210023 China IEEE

In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation *** then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.

关键词： Backstepping consensus multi-agent systems(MASs) neural networks(NNs)

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含有不确定参数结构的数据驱动载荷识别方法

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Acta Mechanica Sinica 2024年第2期40卷 199-213页

作者：崔文旭姜金辉孙慧玉杨泓基王旭王立辉李鸿秋 State Key laboratory of Mechanics and control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing210016China College of Mechanical and Electronic engineering Nanjing Forestry UniversityNanjing210037China Key laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of EducationSoutheast UniversityNanjing210016China Mechatronic engineering College Jinling Institute of TechnologyNanjing211169China

动载荷识别在结构设计和优化中起着至关重要的作用目前的大多数研究都集中在确定性结构上.然而,实际工程结构中的结构参数是未知的.不确定参数的存在会导致载荷识别结果与实际载荷值之间的误差,因此研究不确定结构的动载荷识别问题至关... 详细信息

动载荷识别在结构设计和优化中起着至关重要的作用目前的大多数研究都集中在确定性结构上.然而,实际工程结构中的结构参数是未知的.不确定参数的存在会导致载荷识别结果与实际载荷值之间的误差,因此研究不确定结构的动载荷识别问题至关重要的.本文针对含有不确定参数的结构,提出了一种数据驱动的动载荷识别方法.首先,将不确定参数由一组闭区间向量表征.然后引入卷积神经网络(CNN)对未知载荷区间进行重构.将区间分析理论与泰勒展开相结合,得到了监督载荷的上下边界,并将其用作训练样本.最后,训练后的CNN模型直接识别未知载荷区间的边界.仿真结果表明,该方法具有较高的载荷识别精度和对噪声的鲁棒性.我们构造了一个简支梁结构进行实验,以进一步验证所提出方法在工程中的可行性.此外,我们还讨论了测量点分布和样本数量对识别精度的影响,这有利于在工程实践中的应用。

关键词：数据驱动载荷识别不确定结构不确定参数样本数量泰勒展开测量点分布闭区间

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A generative deep learning framework for airfoil flow field prediction with sparse data

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Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key laboratory of Pattern analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key laboratory of Aerodynamic Noise control Mianyang 621000China State Key laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent *** with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper ***,effective training of the data-driven models in deep learning severely hinges on the data in diversity and *** this paper,we present a novel data augmented Generative Adversarial network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse *** presented approach consists of two modules,pre-training module and fine-tuning *** pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training *** the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the *** use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training *** results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data Supercritical airfoil

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Resilient control for networked control Systems Subject to Cyber/Physical Attacks

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International Journal of Automation and computing 2018年第3期15卷 345-354页

作者： Taouba Rhouma Karim Chabir Mohamed Naceur Abdelkrim Modeling Analysis and Control of Systems(MACS) Laboratory National Engineering School of Gabes (ENIG)University of Gabes

In this paper, we investigate a resilient control strategy for networked control systems（NCSs） subject to zero dynamic attacks which are stealthy false-data injection attacks that are designed so that they cannot be detected based on control input and measurement data. Cyber resilience represents the ability of systems or network architectures to continue providing their intended behavior during attack and recovery. When a cyber attack on the control signal of a networked control system is computed to remain undetectable from passive model-based fault detection and isolation schemes, we show that the consequence of a zero dynamic attack on the state variable of the plant is undetectable during attack but it becomes apparent after the end of the attack. A resilient linear quadratic Gaussian controller, having the ability to quickly recover the nominal behavior of the closed-loop system after the attack end, is designed by updating online the Kalman filter from information given by an active version of the generalized likelihood ratio detector.

关键词： networked control systems (NCSs) cyber physical attacks Kahnan filtering resilient control anomaly detector.

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Preferential attachment network model with aging and initial attractiveness

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Communications in Theoretical Physics 2022年第3期74卷 97-103页

作者： Xiao-Long Peng Complex Systems Research Center Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Center for Applied Mathematics Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Key laboratory of Mathematical Techniques and big data analysis on Disease control and Prevention Shanxi UniversityTaiyuan 030006ShanxiChina

In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of *** network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the *** our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node *** is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network *** show that depending on the aging parameter α two different network topologies can *** αnetwork exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/***,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/*** α>1,such scaling behavior disappears and the degree distribution is exponential.

关键词： complex network model aging preferential attachment power-law behavior

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Online residual useful life prediction of large-size slewing bearings A data fusion method

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Journal of Central South University 2017年第1期24卷 114-126页

作者：封杨黄筱调洪荣晶陈捷 jiangsu Key laboratory of Digital Manufacturing for Industrial Equipment and control Technology(School of Mechanical and Power engineering) Nanjing Tech UniversityNanjing 210009China

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation ***,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and ***,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation ***,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural ***,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction *** of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

关键词：剩余寿命预测数据融合大型回转支承在线寿命预测模型模糊自适应共振理论回转轴承设备运行

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analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

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Energy engineering 2024年第4期121卷 933-949页

作者： Kaicheng Liu Chen Liang Xiaoyang Dong Liping Liu China Electric Power Research Institute Beijing100192China Electric Power Research Institute State Grid Gansu Electric Power CompanyLanzhou730000China State Key laboratory of Power Grid Safety Beijing100084China

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power ***,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various *** paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ ***,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal ***,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other *** results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

关键词： Photovoltaic power generation spatio-temporal prediction temporal convolutional network long short-term memory network

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