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28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

作者： Zhang, Ruijin Wang, Lei Pei, Youzhi Wei, Shaodong Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education Northeastern University Shenyang China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (纸本)9798986576121

During the service of high-speed train, due to the variable environment and completed load conditions, fatigue damage inevitably occurred on key components of train body. Currently, damage tolerance assessment methods based on fracture mechanics are usually used for safety assessment of damaged train body structures. Considering the performance characteristics of aluminum alloy materials, this paper introduces the Failure Assessment Diagram (FAD) method that contemporaneously considers both plastic failure and brittle fracture into the safety assessment of train bodies. By studying the calculation of key parameters for failure evaluation points, 2A assessment curve was established for typical train body structures. Then, taking the draft sill of train body as an example, the safety assessment for the damaged part was conducted by combining finite element simulation analysis and failure assessment curve. Compared with damage tolerance assessment method, FAD method considers the impact of plastic failure and the evaluation results are slightly conservative. © RQD 2023. All rights *** right reserved.

关键词： Fatigue damage

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Data-Driven Frequency Regulation Method for Multi-Terminal HVDC Grids 38

Data-Driven Frequency Regulation Method for Multi-Terminal H...

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38th Youth Academic Annual Conference of Chinese Association of automation, YAC 2023

作者： Yong, Panxiao Jia, Yubin Guo, Jun Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

High voltage direct current (HVDC) lines allow low-loss transmission over long distances. The multi-terminal HVDC network consists of multiple terminals connected by HVDC lines, and each area shares the frequency reserve. Through secondary frequency control, the frequency of AC and DC areas can be regulated back to the set point when the system frequency has deviated. In this paper, a data-driven model predictive control (MPC) strategy is proposed. The Kooman operator is used to obtain the high-dimensional linear model of a nonlinear system, and the Deep Learning method is used to approximate the infinite-dimensional Koopman operator. The nominal MPC is used for optimization and control, and the optimal problem is solved for the Koopman linearized system. The optimal control is applied to the original nonlinear system. Simulation results show the effectiveness of the proposed method. © 2023 IEEE.

关键词： data-driven Koopman operator model predictive control Multi-terminal HVDC

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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A Review of Parameter Estimation Methods of the Three-Parameter Weibull Distribution 9

A Review of Parameter Estimation Methods of the Three-Parame...

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9th International Symposium on system Security, Safety, and Reliability, ISSSR 2023

作者： Yang, Xiaoyu Xie, Liyang Liu, Yaoyao Zhao, Bingfeng Northeastern University School of Mechanical Engineering and Automation Shenyang China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang China

ISBN: (纸本)9798350302479

The Weibull distribution, widely employed in reliability engineering, life testing, and failure analysis, stands as a prominent probability distribution. Notably, the three-parameter Weibull distribution proves invaluable in capturing wear-out failure mechanisms within system modeling. This comprehensive review delves into distinct methodologies and techniques on parameter estimation for the three-parameter Weibull distribution, offering detailed comparisons. © 2023 IEEE.

关键词： Parameter estimation

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Line Feature Based Visual Servo control with TDE for Underwater Vehicle Manipulator systems Target Grasping

Line Feature Based Visual Servo Control with TDE for Underwa...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Yunfei Bian, Xinyu Sun, Yize Zhang, Zongyu Huang, Hai Yang, Xu National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350384185

Underwater vehicle manipulator systems (UVMSs) is becoming a research hotspot in the field of marine intelligent equipment. Introducing visual feedback equipped with cameras in the loop to guide UVMS to complete the grasping has comprehensive advantages of response speed, work efficiency and intelligence. In this paper, mapping between the feature error and the motion of the UVMS generalized degrees of freedom is established to realize the suspended grasping under the coordinated motion of the vehicle body and the manipulator. Then, a UVMS motion control algorithm based on time delay estimation (TDE) is constructed for visual servo autonomous grasping process, the TDE of which is used to estimate and compensate the lumped interference in real time. The Lyapunov theory is applied to analyze its stability and its control performance is verified through simulation experiments. The simulation results shew the effectiveness of the proposed visual servo control with TED method based on line features. © 2024 IEEE.

关键词： Visual servoing

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Research on Robot Path Planning Algorithm for Filter Plate Cleaning of Plate and Frame Filter Press 4

Research on Robot Path Planning Algorithm for Filter Plate C...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Xu, Jie Cui, Long Zhao, Yongguo Tian, Xincheng Center for Robotics School of Control Science and Engineering Shandong University Jinan China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China Institute of Automation Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798331541729

Plate and frame filter press is an important solid-liquid separation equipment that plays an important role in many fields. The filter plate is one of the core components of the frame filter press, and its main function is to support the filter cloth and form a filter chamber. After the filtration process of the filter press is completed, a large number of impurities are often attached to the filter plate, so it is of great value to study the impurity cleaning problem of the filter plate of the plate and frame filter press. In this paper, aiming at the cleaning process of the filter plate of the plate and frame filter press, the image information of the filter plate is obtained by visual means, and then the image is analyzed and processed to obtain the position information of the impurities of the filter plate, the path planning of the obtained position information is carried out, and finally the robot is made to complete the cleaning task efficiently and accurately according to the planned path, which has certain practical value. © 2024 IEEE.

关键词： Robot programming

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Flight Parameter Prediction and Fault Propagation based on Machine Learning and Symbolic Directed Graph 6

Flight Parameter Prediction and Fault Propagation based on M...

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6th International Symposium on Autonomous systems, ISAS 2023

作者： Li, Wenzhuo Guo, Kun Cheng, Yuehua Hu, Hengsong Xu, Cheng Yu, Ziquan Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Institute of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9798350316155

Fault influence and propagation analysis of flight vehicle systems is an important element of flight vehicle health management and also an important problem to be solved. A fault influence model based on data-driven is established, including a prediction model of flight parameters under fault, a dynamic influence path, and an influence degree model. Based on the historical experimental data, a long and short-term memory neural network (LSTM) model is proposed to predict the time-series data of each flight parameter of the flight vehicle under fault;based on the prediction results, a symbolic directed graph (SDG) is used to describe the fault of the flight vehicle system, and then introduce the concept of a compatible path with time-series characteristics to describe the dynamic propagation process of the fault. The case shows that the method proposed in this paper enables qualitative and quantitative analysis of the fault influence, and can reasonably describe the fault propagation path and influence characteristics. © 2023 IEEE.

关键词： Directed graphs

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An online drilling rate of penetration(ROP) optimization method 41

An online drilling rate of penetration(ROP) optimization met...

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第41届中国控制会议

作者： Tuo Wang Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Drilling rate of penetration(ROP) optimization is crucial due to its vital role in increasing the drilling efficiency and decreasing the non-productive *** existing methods seldom pay attention to updating model parameter during drilling,which is actually important for ROP optimization under dynamical and uncertain downhole *** this paper,an online drilling ROP optimization method is proposed via three stages(drilling data pre-processing,online ROP modelling and online optimal operational parameters optimization).In the first stage,drilling data is pre-processed by resampling and Savitzky-Golay *** this,support vector regression and moving-window strategy are introduced together to establish an online ROP model in the next ***,particle swarm optimization algorithm is applied in the online ROP optimization framework to obtain the optimal operational parameters in the last *** results indicated the effectiveness of the proposed method.

关键词： Rate of penetration Drilling optimization Online optimization Moving-window strategy

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

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2023 China automation Congress, CAC 2023

作者： Chen, Kang Du, Shaochong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations. © 2023 IEEE.

关键词： Reinforcement learning

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Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data

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Journal of Environmental Sciences 2023年第8期130卷 126-138页

作者： Hui Ding Yongming Zhao Shenhua Miao Tong Chen Yonghong Liu College of Automation&College of Artifical Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Intelligent Systems Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Provincial Key Laboratory of Intelligent Transport System Guangzhou 510275China Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control Guangzhou 510275China

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple *** the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics of air pollutants(CO,HC,NO_(X),PM_(2.5))on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July *** results showed that the spatial diversity of emissions caused by the unbalanced economywas *** vehicle emissions in the Pearl River Delta region(PRD)with a higher economic level were approximately 1–2 times those in the non-Pearl RiverDelta region(non-PRD).Provincial roads with high loads became potential sources of high ***,emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater *** passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions,contributing more than 50%in the daytime(7:00–23:00)and higher than 26%at night(0:00–6:00).Diesel trucks made up 10%of traffic,but were the dominant player at night,contributed 50%–90%to NO_(X) and PM_(2.5) emissions,with amarked 24-hour change rule of more than 80%at night(23:00–5:00)and less than 60%during ***,targeted control measures by time-section should be set up on collaborative *** findings provide time-varying decision support for variable vehicle emission control on a large scale.

关键词： Intercity roads Dynamic vehicle emissions Vehicle identity detection data Diesel trucks

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