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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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Dense Color Constraints based 6D object pose estimation from RGB-D images 41

Dense Color Constraints based 6D object pose estimation from...

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第41届中国控制会议

作者： Zilun Wang Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd. National Innovation Center of Advanced Rail Transit Equipment

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A key problem for 6 D pose estimation based on RGB-D image input is how to make full use of these two different data *** previous work simply took the depth map as the input of the fourth channel of CNN,or carried out the fusion of features extracted from these two data sources with different *** their fusion did not impose the right constraints and lost some valuable *** this work,we propose that DCC(Dense Color Constraints).6 D pose estimation performance can be improved effectively by using dense corresponding color *** show the most advanced end-to-end performance in LineMod datasets.

关键词： Computer Vision Deep Learning Pose Estimation

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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A Causal Fusion Inference Method for Industrial Alarm Root Cause Analysis Based on Process Topology and Alarm Event Data

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Journal of Beijing Institute of Technology 2022年第4期31卷 371-381页

作者： Pan Zhang Wenkai Hu Xiangxiang Zhang Jianqi An School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China

Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system *** to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of *** the root causes of alarms is beneficial to the decision supports in making corrective alarm *** data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event *** improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among *** topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes *** root cause is then obtained from the directed acyclic graph(DAG)among alarm *** effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.

关键词： roots cause analysis causality inference process topology alarm events

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Quantized impulsive consensus of nonlinear Leader-following multi-agent systems 41

Quantized impulsive consensus of nonlinear Leader-following ...

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第41届中国控制会议

作者： Gang Li Xiaowei Jiang Le You Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This work looks into the quantized impulsive consensus challenges of nonlinear leader-following multi-agent *** graph theory,principle of differential dynamics,logarithmic quantizer,matrix theory,and the Lyapunov function,among other things,are used to investigate the key discoveries on *** appropriate consensus protocols which contain quantized all agents' relative state information and quantized a part of agents' relative state *** the sake of reducing communication consumption and energy,we combine two control strategies and design two control protocols according to the characteristics of quantized control and impulsive *** a series of deductions,we find that under quantized impulsive control,multi-agent systems can achieve consensus as long as certain sufficient conditions are ***,the simulation example demonstrates the validity and correctness of this papers theoretical approach.

关键词： Multi-agent systems Leader-following Quantized impulsive consensus

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Modeling for coke quality prediction using Gaussian function and SGA

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Science China(Information Sciences) 2022年第1期65卷 282-284页

作者： Yan YUAN Qilin QU Weihua CAO Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Dear editor,Recently, we made some new attempts at predicting coke quality. Coke quality prediction provides important guidance for coal blending, improving the quality of coke, and reducing the cost. Prediction usually involves selection of the coal blending parameters and selection of the modeling methods. In recent years, traditional coal quality indicators such as coal impurity(ash Ad, sulfur St.d),

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A Pipeline Leak Detection Method Based on MGD-KLD and On-delay Timer

A Pipeline Leak Detection Method Based on MGD-KLD and On-del...

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第42届中国控制会议

作者： Jiaqi He Yan Yuan Wenkai Hu Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Leaks in natural gas pipelines can cause very serious safety accidents, and timely detection and remedial action can greatly reduce the losses. In recent years, pipeline leak detection has received extensive studies. Most methods use pressure sensors or acoustic sensors to detect pipelines, but there are certain limitations on the usage scenarios and detection time *** this basis, this paper selects maglev vibration detector to detect the vibration signal of pipelines. The difficulty lies in that,sudden changes in vibration signals due to external disturbances, may lead to false alarms. Therefore, this paper proposes a pipeline leak detection method using Multivariate Gaussian Distribution based Kullback-Leibler Divergence(MGD-KLD) and on-delay timer to reduce false alarms during the detection process. In this paper, by constructing a simulated pipeline platform for leak experiments and applying the above method to process the experimental data, the false alarm rate of pipeline leak detection can be effectively reduced.

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control of Hybrid Energy Storage Based on Variable Droop Coefficient in DC Microgrid

Control of Hybrid Energy Storage Based on Variable Droop Coe...

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第42届中国控制会议

作者： Kun Song Danyun Li Dashuang Zhao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For hybrid energy storage systems in DC microgrids,a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components,then assign them to batteries and supercapacitors to respond ***,aiming at the service life of the energy storage system,this paper considers the characteristics and key parameters of the hybrid energy storage structure and proposes an adaptive drooping comprehensive control strategy considering the SOC of the energy storage unit given the shortcomings of power distribution within the current hybrid energy *** to the self-regulation capacity of each energy storage unit,it is sorted and constrained,and protected by using SOC,which ensures the economy and safety of the system while ensuring power *** traditional droop control and adaptive droop control are simulated to verify the effectiveness of the proposed control strategy.

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Company Ltd. Operation Management Department Jiangxi Nanchang330000 China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Stability Analysis of Delayed T-S Fuzzy systems via the Fuzzy Line-Integral Method 42

Stability Analysis of Delayed T-S Fuzzy Systems via the Fuzz...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Zhou-Zhou Jin, Li He, Yong School of Automation China University of Geosciences 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

ISBN: (纸本)9789887581543

This paper focuses on the problem of stability analysis for Takagi-Sugeno systems with time-varying delays. Firstly, a suitable Lyapunov-Krasovskii functional (LKF) containing fuzzy line-integral Lyapunov functional is constructed, which can introduce membership functions information while avoiding emerging the time-derivatives of membership functions. Then, a generalized free-matrix-based integral inequality is applied to estimate the derivative of the LKF. As a result, a less conservative stability criterion is obtained. Finally, a numerical example is carried out to illustrate the effectiveness and merits of our method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Membership functions

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