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第36届中国控制会议

作者： ZONG Xiaofeng LI Tao ZHANG Ji-Feng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice Department of MathematicsEast China Normal University Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper is to study the stabilizability and stabilization issues of linear dynamical systems based on the delayed and noisy feedback control. For the general linear systems, the necessary conditions and sufficient conditions for mean square and almost sure stabilizability are deduced and the corresponding feedback controls are designed according to the generalized algebraic Riccati equation. It is revealed that the stabilizability is up to the system parameters（delays, noises, and unstable eigenvalues of the original system） and unstable systems can be stabilized in mean square if the feedback control is disturbed by noises with small intensities. It is showed that second-order integrator systems must be stabilizable for any given noise intensity and delay.

关键词： Stabilization Feedback control Noise Delay

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Brain Storming Optimization Algorithm for Heating Dispatch Scheduling of Thermal Power Plant 29

Brain Storming Optimization Algorithm for Heating Dispatch S...

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第29届中国控制与决策会议

作者： Lu Kang Yali Wu Xinrui Wang Xiaohua Feng School of Automation and Information Engineering Xi'an University of Technology Shannxi Province Key Laboratory of Complex System Control and Intelligent Information Processing School of Marine Science and Technology Northwestern Polytechnical University

ISBN: (纸本)9781509046584

In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving and emission-reduction *** this paper,an optimal heat load dispatching model is set up under the premise of power load distribution of thermal power *** kinds of heat loads including to high-pressure,medium-pressure and low-pressure are taken into account in the scheduling *** to the characteristics of the problem,Brain Storming Optimization(BSO) algorithm is used to get the optimal distribution *** comparison with the original scheme shows the correctness of the model and the optimization ***,the optimization ability and effectiveness of brain storming optimization algorithm in solving complex optimization problems are verified by comparing the proposed algorithm with the other different optimization algorithms,such as Particle Swarm Optimization(PSO) and Differential Evolution(DE) algorithm.

关键词： Heating Dispatching Brain Storming Optimization Algorithm Load Distribution

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Optimization Oriented Performance Assessment for Combustion Process of Coke Oven

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IFAC-PapersOnLine 2017年第1期50卷 13778-13783页

作者： Lei, Qi Wu, Min Liu, Guoping School of Information Science and Engineering Central South University ChangshaHunan410083 China School of Automation China University of Geosciences WuhanHubei430074 China School of Engineering University of Southwales PontypriddCF37 1DL United Kingdom Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanHubei430074 China CTGT Center Harbin Institute of Technology HaerbinHeilongjiang150001 China

This paper presents an optimization oriented performance assessment method for combustion process of coke oven by integrating analytic hierarchical process (AHP) and intuitionistic fuzzy(IF) set. An AHP model is built up including safety, stability and economic considerations. To handle the complexity in the combustion process of coke oven, where the decision maker usually has some uncertainty in assigning preference values to the index considered, the IF set is used to obtain the IF preference. Entropy and cross entropy are applied to calculate the weights of the individual indices. The comprehensive evaluation method integreated the AHP and IF can effectively solve the performance assessment for the combustion process of the coke oven. Finally, the data obtained from the process model in different operating conditions are used to demonstrate the effectiveness. © 2017

关键词： Coke ovens Coke Combustion Decision making Fuzzy sets Hierarchical systems Analytic hierarchical process Combustion pro cess Comprehensive evaluation Different operating conditions Economic considerations Entropy methods Intuitionistic fuzzy Performance assessment

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H∞ state estimation for discrete time-delayed stochastic genetic regulatory networks with known sojourn probabilities 23

H∞ state estimation for discrete time-delayed stochastic ge...

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23rd IEEE International Conference on automation and Computing, ICAC 2017

作者： Wan, Xiongbo Wang, Zidong Ren, Chuanyu School of Automation China University of Geosciences Wuhan Hubei430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China Department of Computer Science Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom

ISBN: (纸本)9780701702618

This paper investigates the problem of H∞ state estimation for discrete-time stochastic genetic regulatory networks (GRNs) with switching parameters, time-varying delays, and exogenous disturbances. A new discrete-time stochastic GRN model with switching parameters is proposed by assuming that the sojourn probability of each subsystem of the GRN under consideration is known. By using the stochastic analysis approach, sufficient conditions are derived under which the augmented estimation error system is stochastically stable and satisfies a prescribed H∞ disturbance attenuation level γ. The gain matrices of the desired state estimator can be obtained by solving a matrix inequality. A numerical example is given to illustrate the effectiveness of the designed state estimator. © 2017 Chinese automation and Computing Society in the UK - CACSUK.

关键词： State estimation

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Stability and control of Nonlinear Time-varying systems 1

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2018年

作者： Shuli Guo Lina Han

ISBN: (数字)9789811089084

ISBN: (纸本)9789811089077;9789811342691

This book presents special systems derived from industrial models, including the complex saturation nonlinear functions and the delay nonlinear functions. It also presents typical methods, such as the classical Liapunov and Integral Inequalities methods. Providing constructive qualitative and stability conditions for linear systems with saturated inputs in both global and local contexts, it offers practitioners more concise model systems for modern saturation nonlinear techniques, which have the potential for future applications. This book is a valuable guide for researchers and graduate students in the fields of mathematics, control, and engineering.

关键词： control and systems Theory systems Theory, control Mathematical Modeling and Industrial Mathematics Mathematical Applications in Computer Science Mathematics of Algorithmic complexity

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Event-triggered consensus for linear continuous-time multi-agent systems based on a predictor

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Information Sciences 2018年 459卷 278-289页

作者： Jian Sun Qiuling Yang Xiaoyu Liu Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 PR China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 PR China China North Industrial Group Corporation Limited China Ordnance Industry Navigation and Control Technology Institute Beijing 100089 PR China

In this paper, the problem of an event-triggered consensus for a linear continuous-time multi-agent system is investigated. A new event-triggered consensus protocol based on a predictor is proposed to achieve consensus while not requiring continuous communication among agents. The predictor utilizes an artificial closed-loop system to predict the future state of each agent. With the proposed consensus protocol, each agent only needs to monitor its own states to determine its event-triggered instants. When an event of an agent is triggered, the agent immediately updates its consensus protocol and sends its state information to its neighbors. When an agent receives state information from its neighbors, the agent immediately updates its consensus protocol and predictor. A necessary and sufficient condition that solves the consensus problem is derived. Moreover, it is proved that Zeno behaviors are excluded. Finally, some numerical examples are given to illustrate that, with the proposed protocol, a multi-agent system can achieve consensus while greatly reducing event-triggered times.

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An Improved Result on H∞ State Estimation for Static Neural Networks with Time-Varying Delay

An Improved Result on H∞ State Estimation for Static Neural...

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第36届中国控制会议

作者： JIN Li HE Yong ZHANG Chuan-Ke WU Min School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents an improved result on H state estimation for static neural networks with a time-varying ***,a novel Lyapunov-Krasovskii functional(LKF) with several augmented terms is ***,a relaxed integral inequality is employed to make a tight estimation for single integral terms with time-varying delay in the derivative of the *** a result,a delay-dependent criterion in terms of linear matrix inequalities is ***,two numerical examples are given to demonstrate the benefits and effectiveness of the obtained criterion.

关键词： Static neural networks Time-varying delay H∞ state estimation Lyapunov-Krasovskii functional Relaxed integral inequality Linear matrix inequality

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A prediction model of edge gas flow development state based on BP neural network 5

A prediction model of edge gas flow development state based ...

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5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Zhang, Jialiang An, Jianqi Wu, Min Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

The development and distribution states of the blast furnace (BF) gas flow determine whether the reaction in the furnace can proceed normally and decide the utilization efficiency of the carbon in the furnace and the stable smooth operation of the furnace. The internal environment of BF is closed with high temperature and high pressure, which causes that the relationship between the development situation of edge gas flow of the BF and the BF operation is uncertain. In this paper, a prediction model of edge gas flow development state based on BP neural network (BPNN) is proposed, clarifying the relationship between the development state of edge gas flow and the edge burden parameters. Firstly, this paper gets the relative operation factors of effecting the development situation of edge gas flow through analyzing the process mechanism of the BF ironmaking, and proposes the development index of edge gas flow and the heat load of cooling stave to measure the development state of the edge gas flow. Secondly, the intrinsic correlation between the data is verified, and the data are analyzed by Spearman's rank correlation coefficient (SRCC) analysis. Finally, according to the analysis results, combined with the burden mechanism, the edge gas flow prediction model is established to realize the prediction of the development state of the edge gas flow. The calculation of the data collected in the metallurgy scene of the BF suggested that this prediction model of the edge gas flow development state of the BF could describe the relationship between BF operation parameters and the developing state of the edge gas flow.

关键词： Flow of gases

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Blockchain based digital asset management system architecture for power grid big data

arXiv

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arXiv 2018年

作者： Jun, Zhang Fei-Yue, Wang State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Electrical Engineering Wuhan University Wuhan430072 China Qingdao Academy of Intelligent Industries Qingdao266071

Chinese power grid enterprises are in need for development of digital asset management system. The characteristics of decentralization, self-trust and self-confidence, pave a promising technical path for power grid digital assess management and Big Data applications. This article firstly introduces the state-of-the-art of power grid Big Data and digital asset management, and the related issues in power grid enterprises. The solution of blockchain based digital asset management technology and its implementation are presented in details, followed by a discussion of their future developmental directions. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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An Online Modeling Method for Formation Drillability Based on OS-Nadaboost-ELM Algorithm in Deep Drilling Process

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IFAC-PapersOnLine 2017年第1期50卷 12886-12891页

作者： Gan, Chao Cao, Weihua Wu, Min Chen, Xin Hu, Yule Wen, Guojun Gao, Hui Ning, Fulong Ding, Huafeng School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Adavanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences Wuhan430074 China School of Mechanical Engineering & Electronic Information China University of Geosciences Wuhan430074 China

To achieve safety, high quality, and efficiency in deep drilling, it is necessary to get formation drillability around the borehole during drilling-trajectory planning and intelligent drilling control. Since the drilling data have the characteristics of low value density and noise in the process of deep drilling, it is difficult to model formation drillability in deep drilling. In this paper, a new online modeling method for formation drillability based on online sequential nadaboost extreme learning machine (OS-Nadaboost-ELM) algorithm has been proposed. Firstly, the well logging parameters are chosen as the inputs of the model, whose output is formation drillability. Then, several ELM models are established and the outputs of these models are as weak learners. Then the weak learners are combined by Nadaboost algorithm in order to get a strong learner. Finally, the recursive least squares algorithm is used to adjust the model. The numerical test results show that, in both prediction accuracy and training efficiency aspects, the proposed method is better than other prediction methods such as multiple regression, gray method, back-propagation neural networks, Nadaboost extreme learning machine and online sequential extreme learning machine. Thus the prediction model serves as the online geological model to develop intelligent drilling systems. © 2017

关键词： E-learning

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