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

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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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1064-1074页

作者： Ying Wang Chao Lu Xinran Zhang School of Technology Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China IEEE

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power ***,it also brings new *** the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application *** order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this ***,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage *** begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of ***,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.

关键词： Ambient signal CNN field PMU data load model parameter selection short-term voltage stability

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Deep reinforcement learning guidance with impact time control

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Journal of Systems engineering and Electronics 2024年第6期35卷 1594-1603页

作者： LI Guofei LI Shituo LI Bohao WU Yunjie School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Beijing Institute of Control and Electronic Technology Beijing 100038China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In consideration of the field-of-view(FOV)angle con-straint,this study focuses on the guidance problem with impact time control.A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle *** basis of the framework of the proportional navigation guidance,an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm,in which the reward functions are developed to decrease the time-to-go error and improve the terminal guid-ance *** numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.

关键词： impact time deep reinforcement learning guidance law field-of-view(FOV)angle deep deterministic policy gradient

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Event-triggered consensus control of heterogeneous multi-agent systems: model-and data-based approaches

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science China(Information sciences) 2023年第9期66卷 151-167页

作者： Xin WANG Jian SUN Fang DENG Gang WANG Jie CHEN School of Automation Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Chongqing Innovation Center Department of Control Science and Engineering Tongji University

This article addresses the model-and data-based event-triggered consensus of heterogeneous leader/follower multi-agent systems(MASs). A dynamic periodic transmission protocol is developed to alleviate the communication and computational burden, where the followers can interact locally with neighbors to approach the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived as linear matrix inequalities(LMIs), along with a design method for obtaining distributed event-triggered controllers and the associated triggering *** collecting noise-corrupted state-input measurements in offline open-loop experiments, a data-based leader/follower MAS representation is derived and employed to address the data-driven consensus control problem without explicit MAS models. This result is subsequently generalized to guarantee an H∞-consensus control performance. Finally, a simulation example is given to corroborate the efficiency of the proposed distributed triggering scheme and the data-driven consensus controller.

关键词： data-driven control multi-agent system consensus looped-functional LMI

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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

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ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Learning-Observer-Based Adaptive Tracking control of Multiagent Systems Using Compensation Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 358-369页

作者： Li, Zhiqiang Liu, Yang Ma, Hui Li, Hongyi Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Mathematics and Statistics Guangzhou510006 China

This article focuses on the adaptive consensus tracking problem for nonlinear multiagent systems with unmeasurable state and input saturation. For unmeasurable state and nonlinearities, a learning-observer inspired by the idea of extended state observer and the learning ability of neural network are established to observe the reconstructed system. In view of input saturation, a nonlinear auxiliary system with the same order is constructed to obtain the compensation signals. Different from the previous auxiliary system, the proposed method simultaneously compensates the change of nonlinearity caused by input saturation. Subsequently, a consensus tracking error is designed to ensure that the follower tracks the neighbor compensated output. Moreover, an adaptive consensus tracking control method based on learning-observer and compensation mechanism is proposed under the backstepping framework. The stability analysis is given by virtue of the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through the simulation of unmanned air vehicles with one virtual leader and four followers. © 2020 IEEE.

关键词： Neural networks

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Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

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Energy engineering 2025年第1期122卷 63-84页

作者： Suliang Ma Zeqing Meng Mingxuan Chen Yuan Jiang School of Electrical and Control Engineering North China University of TechnologyBeijing100144China ChinaThree Gorges Technology Co. Ltd.Beijing100032China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is ***,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical *** deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s *** optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization *** proposed method better balances all optimization objective functions and reduces the impact of their *** the cost of BESS decreases by approximately 30%,its participation deepens by about 1 ***,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase *** study aims to provide a more reasonable approach to solving multi-objective optimization problems.

关键词： Electro-hydrogen system multi-objective optimization standardization method hybrid energy storage system

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Non-singular Fast Terminal Sliding Mode and Active Disturbance Rejection control for Quadrotor UAV

Non-singular Fast Terminal Sliding Mode and Active Disturban...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Chen, Cheng Liu, Yunping Zhang, Tingting Sun, Yuanxin Shi, Jingwei School of Automation Nanjing University of Information Science and Technology Nanjing China School of Command and Control Engineering Army Engineering University of PLA Nanjing China

ISBN: (纸本)9798350384185

The purpose of this paper is to improve the anti-interference ability of the quadrotor UAV. This paper proposes a non-singular fast terminal sliding mode combined with an active disturbance rejection controller. Firstly, the uncertainty factors, such as model errors and internal and external disturbances of the quadrotor UAV system, are regarded as lumped disturbances, estimated and compensated online by the extended state observer in real-time. According to its underactuated, multivariable, and nonlinear characteristics, it is divided into a position loop and an attitude loop. On this basis, it is transformed into complete drive control by introducing virtual control quantity. The simulation results show that the non-singular fast terminal sliding mode active disturbance rejection controller has better robustness than the cascade active disturbance rejection control. © 2024 IEEE.

关键词： Disturbance rejection

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