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第36届中国控制会议

作者： Wei-Jie Tang Zhen-Tao Liu Qian Wang School of Industrial Automation Beijing Institute of Technology School of Automation China University of Geosciences Hubei key Laboratory of Advanced control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

Speed control mode of a DC motor without knowing the specific parameters of the motor is *** approximate mathematical model of the control system is obtained by the system identification when the output of the system is measured by loading the specific input *** PID control algorithm is adopted and the P,I,and D parameters are obtained by auto *** in the loop（HIL） experiments are carried out on MATLAB and Arduino platform,in which the experimental results demonstrate the feasibility of the proposal.

关键词： DC motor system Identification PID control HIL Experiment Automatic Code Generation

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A Soft-Sensing Model for Oxygen-Content in Flue Gases of Coal-Fired Power Plant Based on Neural Network

A Soft-Sensing Model for Oxygen-Content in Flue Gases of Coa...

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第37届中国控制会议

作者： Xin Chen Jingcheng Wang Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

As the main force of power generation in China, coal-fired power plants have great significance for energy conservation and emission reduction. The oxygen content in flue gases is an important index to reflect the combustion efficiency of the boiler, and the accurate monitoring is an effective way to improve combustion efficiency. Based on the BP neural network, a soft-sensing model for oxygen content in flue gases is proposed in this paper, this article, taking a boiler combustion system of 600 MW coal-fired power plant unit in Jiaxing, Zhejiang as the research object, through real-time measurement data of DCS system, a flue gases oxygen content model is established, and this model has realized the soft measurement to flue gases oxygen content, the simulation results prove the validity of the soft sensing model. This model has great significance to the monitoring of the oxygen content in flue gases of boiler combustion system and the further study of combustion optimization on this basis.

关键词： BP Neural Network Flue Gases Oxygen Content Soft Sensing Model

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key Technologies of Time-Sensitive Networking and Its Application

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IOP Conference Series: Materials Science and Engineering 2020年第4期782卷

作者： Qing Li Dong Li Xinbo Sun Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang 110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China

Network is the core element of many industrial and automation systems. In the future, more attention will be paid to convergent and unified network. Time-critical traffic and non-time-critical traffic need to share communication channels. The goal of the IEEE TSN task group is to extend the existing Ethernet standard to achieve a degree of certainty that meets the hard-real-time requirements of modern control networks in the industrial automation and automotive industries. Time sensitive network (TSN) is characterized by low jitter, low delay and deterministic transmission. It can transmit critical and non-critical traffic in the same network, which is very suitable for time- critical applications with high requirements on transmission delay. This paper briefly summarizes the key components of TSN, studies the key technologies, and carries out a simple traffic scheduling experiment on the switch based on 802.1Qbv function, and finally analyzes the application of TSN in industrial automation and automobile industry.

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UKF based Fault Detection and State Estimation for Nonlinear systems with Correlated Noise 36

UKF based Fault Detection and State Estimation for Nonlinear...

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第36届中国控制会议

作者： Hongxue Chen Liping Yan Yuanqing Xia Mengyin Fu Bo Xiao Guangzheng Hu Huifang Li Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781538629185

State estimation and fault diagnosis are essential topics for dynamic *** Kalman fllter（UKF） has been widely applied in nonlinear *** classical UKF algorithm is built on the premise that process noise and measurement noise is *** practical problems,this assumption is not always *** addition,due to the limitation of communication and sensor fault,etc.,data missing or unreliable measurements will happen ***,it is very important to study the state estimation of nonlinear systems with unreliable measurements and correlated *** this paper,an UKF based state estimation algorithm with unreliable observations under correlated noise is presented.A numerical example is given to show the feasibility and effectiveness of the presented algorithm.

关键词： fault detection state estimation correlated noise nonlinear system

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A smooth double proximal primal-dual algorithm for a class of distributed nonsmooth optimization problem

arXiv

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arXiv 2018年

作者： Wei, Yue Fang, Hao Zeng, Xianlin Chen, Jie Pardalos, Panos M. School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China Industrial and Systems Engineering University of Florida

This technical note studies a class of distributed nonsmooth convex consensus optimization problem. The cost function is a summation of local cost functions which are convex but nonsmooth. Each of the local cost functions consists of a twice differentiable (smooth) convex function and two lower semi-continuous (nonsmooth) convex functions. We call this problem as single-smooth plus double-nonsmooth (SSDN) problem. Under mild conditions, we propose a distributed double proximal primal-dual optimization algorithm. The double proximal operator is designed to deal with the difficulty caused by the unproximable property of the summation of those two nonsmooth functions. Besides, it can also guarantee that the proposed algorithm is locally Lipschitz continuous. An auxiliary variable in the double proximal operator is introduced to estimate the subgradient of the second nonsmooth function. Theoretically, we conduct the convergence analysis by employing Lyapunov stability theory. It shows that the proposed algorithm can make the states achieve consensus at the optimal point. In the end, nontrivial simulations are presented and the results demonstrate the effectiveness of the proposed algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost functions

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Mathematical model of brain tumour growth with drug resistance

arXiv

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arXiv 2020年

作者： Trobia, José Tian, Kun Batista, Antonio M. Grebogi, Celso Ren, Hai-Peng Santos, Moises S. Protachevicz, Paulo R. Borges, Fernando S. Szezech, José D. Viana, Ricardo L. Caldas, Iberê L. Iarosz, Kelly C. Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Graduate in Science Program - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’anPR710048 China Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom Xi’an Technological University Xi’anPR710021 China Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Department of Physics Federal University of Paraná CuritibaPR80060-000 Brazil Faculdade de Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil

Brain tumours are masses of abnormal cells that can grow in an uncontrolled way in the brain. There are different types of malignant brain tumours. Gliomas are malignant brain tumours that grow from glial cells and are identified as astrocytoma, oligodendroglioma, and ependymoma. We study a mathematical model that describes glia-neuron interaction, glioma, and chemotherapeutic agent. In this work, we consider drug sensitive and resistant glioma cells. We show that continuous and pulsed chemotherapy can kill glioma cells with a minimal loss of neurons. © 2020, CC BY.

关键词： Tumors

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Fault Location in Active Distribution Network Based on Convariance Matrix and PMU Data

Fault Location in Active Distribution Network Based on Conva...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Xiangnv Wang Linlin Zhang Fang Shi Hengxu Zhang Yong Zhang Jian Zhou The Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China State Grid Shanghai Municipal Electric Power Company Shanghai China

Fault diagnosis and location of distribution network is a difficult problem. With the integration of the distributed generation, the multi-source of the fault current makes the problem more complex. This paper proposes a fault diagnosis method based on synchronized measurement information matrix, which includes two procedures: 1) to detect the occurrence of the fault; 2) to locate the fault section and determine the fault type. Firstly, the local covariance matrix is defined by introducing sliding window based on synchronous phasor information. Then, a local information covariance matrix is constructed by taking each PMU's data as a sample at a certain moment to detect the occurrence of the fault. The fault segment is located and the fault type is determined via improving the local covariance matrix. Finally, a simulation example is used to verify the effectiveness and reliability of the proposed method.

关键词： Covariance matrices Phasor measurement units Symmetric matrices Current measurement Fault location Fault currents

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Optimal Corrective control for Bipolar Multi-terminal HVDC Grid

Optimal Corrective Control for Bipolar Multi-terminal HVDC G...

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IEEE General Meeting Power& Energy Society

作者： Jiahong Li Hua Ye Yutian Liu Yanling Du Haitao Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University) Ji’nan China State Grid Jibei Electric Power Company Beijing China

An outage of DC transmission lines or converters occurs in bipolar multi-terminal HVDC grid (MTDC) may result in security violation. Corrective control strategies are therefore needed to ensure the operational security of the system. For MTDC grid following N-1 contingencies, a power shift-based optimal corrective control method is presented in this paper. Certain power are shifted from the faulty pole to the normal pole or among different converter stations within a pole, so that the amount of renewable energy accommodation of the MTDC is maximized. The method is intensively studied on a modified New England 10-machine 39-bus test system with inclusion of a four-terminal bipolar DC grid.

关键词： Renewable energy sources Power transmission lines Security Generators Power generation HVDC transmission Load flow

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 177-185页

作者： Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects *** the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor *** research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral *** involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition *** maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects *** performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关键词： distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification

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