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arXiv 2020年

作者： Su, Wei Wang, Xueqiao Chen, Ge Shen, Kai School of Automation and Electrical Engineering University of Science and Technology Beijing & Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China National Center for Mathematics Interdisciplinary Sciences & Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China

Recently the theory of noise-induced synchronization of Hegselmann-Krause (HK) dynamics has been well developed. As a typical opinion dynamics of bounded confidence, the HK model obeys a synchronous updating rule, i.e., all agents check and update their opinions at each time point. However, whether asynchronous bounded confidence models, including the famous Deffuant-Weisbuch (DW) model, can be synchronized by noise have not been theoretically proved. In this paper, we propose a generalized bounded confidence model which possesses a stochastic asynchronous rule. The model takes the DW model and the HK model as special cases and can significantly generalize the bounded confidence models to practical application. We discover that the asynchronous model possesses a different noise-based synchronization behavior compared to the synchronous HK model. Generally, the HK dynamics can achieve quasi-synchronization almost surely under the drive of noise. For the asynchronous dynamics, we prove that the model can achieve quasi-synchronization in mean, which is a new type of quasi-synchronization weaker than the "almost surely" sense. The results unify the theory of noise-induced synchronization of bounded confidence opinion dynamics and hence proves the noise-induced synchronization of DW model theoretically for the first time. Moreover, the results provide a theoretical foundation for developing noise-based control strategy of more complex social opinion systems with stochastic asynchronous rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

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Energy Procedia 2019年 158卷 1798-1803页

作者： Tao Xue Xiaolong Wu Yuanwu Xu Suwen Jing Zehua Li Jianhua Jiang Zhonghua Deng Xiaowei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

关键词： SOFC fault diagnosis neural network

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Structural Performance Analysis of Wall Climbing Robot 2

Structural Performance Analysis of Wall Climbing Robot

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2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and automation, AMIMA 2019

作者： Huang, Li Liao, Shangchun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot. © Published under licence by IOP Publishing Ltd.

关键词： Robot applications

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Binary Channel Fuzzy Self-Adjusted Neural Network for Solving Time-Changing QP Problems

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IEEE transactions on neural networks and learning systems 2025年 PP卷 PP页

作者： Yamei Luo Qingyi Ren Zihao Zheng Siyuan Chen Xin Ma Yu Liu Xiaoli Li Junzhi Yu Zhijun Zhang The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern University of Science and Technology Institute for Robotics Shenzhen518055 China Peking University College of Engineering Beijing100871 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Laboratory Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong 723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established; then, using Lagrange's law, the time-changing QP problem is transformed into a time-changing matrix equation; and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN).

关键词： Recurrent neural networks

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A New Intrusion Detection Method Based on Adaptive Feature Extraction

A New Intrusion Detection Method Based on Adaptive Feature E...

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第三十八届中国控制会议

作者： Ya-Li Wu Guo-Ting Li Yu-Long Fu Xiao-Peng Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Compared with traditional machine learning, intrusion detection based on deep learning has the advantage of feature extraction. Auto-encoders, owing to having good feature extraction and dimensionality reduction ability, are known for the superior performance. However, the network structure selection is time-consuming and laborious. The essential features cannot be automatically extracted according to different distribution characteristics of the data set. Moreover, the existing data pre-processing method fails to adapt the model better. Based on the stack sparse auto-encoders(SSAE), we propose a new intrusion detection model named DBSO-SSAE. The algorithm is based on difference brain storm optimization(DBSO) which automatically adapts to selection network framework. Meanwhile, a new normalization strategy is introduced to further improve the accuracy of the model. The simulation results show that DBSO-SSAE greatly reduces the complexity of manual tuning and increases the adaptability of the network model.

关键词： intrusion detection feature extraction SSAE deep learning difference brain storm optimization

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Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

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Chinese control Conference (CCC)

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant (LDTI) systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Generators System performance Benchmark testing Sensitivity Degradation Fault detection Aerospace electronics

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Transformer Winding Deformation Diagnosis Based on Signal Distance

Transformer Winding Deformation Diagnosis Based on Signal Di...

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第30届中国过程控制会议（CPCC 2019）

作者： LiShengmin ZhaoDan HeYuanyuan WangXiaoafan School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

Aiming at the problem that the correlation coefficient method cannot judge the deformation of the transformer winding when the measured frequency response curve is the same as the reference frequency response curve(frequency response analysis,FRA) and the amplitude is different, this paper proposed a new transformer winding diagnosis method based on signal *** method can accurately determine whether the winding is deformed according to the mutual distance value [0, 1] by calculating the mutual distance between the measured frequency response curve and the reference frequency response *** the simulation experiment and the national standard judgment criterion, the criteria for judging the deformation of the winding by mutual distance were *** simulation results show that the method is sensitive, reliable and universal.

关键词： FRA Frequency response curve Winding deformation signal distance mutual distance

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Reaching Non-Negative Edge Consensus of Networked Dynamical Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2712-2722页

作者： Wang, Xiao Ling Su, Housheng Chen, Michael Z. Q. Wang, Xiao Fan Chen, Guanrong College of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Automation Nanjing University of Science and Technology Nanjing China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic Engineering City University of Hong Kong Hong Kong

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

关键词： Distributed algorithm line graph low-gain feedback networked system non-negative edge consensus

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Standby and Shutdown Cycles Modeling of SOFC Lifetime Prediction

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Energy Procedia 2019年 158卷 1573-1578页

作者： Wu Xiao-long Xu Yuanwu Xue Tao Zhao Dongqi Jiang Jianhua Deng Zhonghua Fu Xiaowei Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057 China

In order to more accurately predict the impact of solid oxide fuel cells to stack life on standby and shutdown, this paper proposes a modeling method based on experimental data. Which is based on Elman neural network(NN). At the same time, a main factor is considered in the modeling process which is the effect of cooling rate on the stack. In the process of modeling, the most obvious cooling rate is used to modeling. There are two different influencing factors on classify, the training set and the verification set respectively. After the reliability of the model, the Solid Oxide Fuel cell (SOFC) stack life prediction is carried out. From the predict results and the experimental results, it is found that the prediction results are good and the high precision.

关键词： Solid Oxide Fuel Cell(SOFC) Stack useful lifetime predict Cooling speed Gas composition Cycle standby shutdown

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Modeling and simulation of temperature distribution for planar cross-flow solid oxide fuel cell

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Energy Procedia 2019年 158卷 1585-1590页

作者： Yuan-wu Xu Xiao-long Wu Hang You Tao Xue Dong-qi Zhao Jian-hua Jiang Zhong-hua Deng Xiao-wei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Research Institute of Huazhong University of Science & Technology in Shenzhen Shenzhen 518057 China

Solid oxide fuel cell is a device that can convert chemical energy directly into electricity. Its advantages, such as high efficiency, low emission, quiet operation, fuel flexibility, bring about the broad application prospect. However, it is difficult to obtain the temperature distribution in the existing planar cross flow solid oxide fuel cell through the experiment. In this paper, a control orient two-dimensional differential equation model is established for a planar cross flow solid oxide fuel cell based on the finite node method, and an iterative algorithm for calculating the real time voltage of the fuel cell is proposed for the model. Based on the model, the temperature distribution of the fuel cell in the test and system configuration is simulated. The simulation results show that the model can reflect the thermoelectric characteristics of the planar cross flow solid oxide fuel cell, especially the temperature distribution of the fuel cell. The SOFC temperature distribution modeling in this paper is helpful for the development of temperature distribution observers and design related control methods in later studies.

关键词： modeling simulation temperature distribution planar cross flow solid oxide fuel cell

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