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第35届中国控制会议

作者： Ling Wang Xi-yuan Xiao Jian-yao Meng School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education)

ISBN: (纸本)9781509009107

Air pollution is a growing problem arising from various activities and events. Monitoring and prediction of air pollution index remains an important public health concern. In this paper, FCM-HMM Multi-model is proposed to model the air pollution atmosphere system. First, FCM-HMM clustering is developed to mine the inherent states behind the meteorological observation sequence. Second, TS fuzzy inference multi-model is constructed for each state to predict the air pollution index. The experiment results proved the feasibility of the model on air pollution data in Beijing.

关键词： Hidden markov model Fuzzy c-means TS fuzzy inference multi-model Air pollution index(API)

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Tracking of Stribeck Friction based on Second-Order Linear Extended State Observer

Tracking of Stribeck Friction based on Second-Order Linear E...

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第28届中国控制与决策会议

作者： Lijun Wang Qing Li Ruihua Jiao Yixin Yin Yinan Feng Yichang Liu School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process(Ministry of Education) University of Science and Technology Beijing

ISBN: (纸本)9781467397155

To deal with the problem of observing or tracking for discontinuous signal,a kind of second order linear extended state observer(LESO) algorithm is *** ESO estimating the uncertain disturbance,a simple second-order linear ESO with less tunable parameters was designed in the absence of dynamic model,and the method of discrete time realization was *** proposed LESO was applied to the tracking of discontinuous Stribeck Friction in comparison with adaptive state *** results show that the proposed ESO is effective and practical.

关键词： Extended State Observer Adaptive State Observer Stribeck Friction Discontinuous Track

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Fault detection and identification for a class of nonlinear systems with model uncertainty

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Applied Mathematical Modelling 2016年第15-16期40卷 7368-7381页

作者： Bingyong Yan Housheng Su Wei Ma School of Automation Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology 130 Meilong Road Shanghai 200237 China School of Automation Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory for Advanced Materials & Institute of Fine Chemicals East China University of Science and Technology 130 Meilong Road Shanghai 200237 China

In this paper, we present a novel fault detection and identification (FDI) scheme for a class of nonlinear systems with model uncertainty. At the heart of this approach is an on-line approximator, referred to as fault tracking approximator (FTA). Differently from the other approximators, the FTA uses iterative algorithms to detect and identify nonlinear system faults, even in the presence of model uncertainty, which is motivated by predictive control theory and iterative learning control theory. The FTA can simultaneously detect and identify the shape and magnitude of the faults. The rigorous stability analysis and fault tracking properties of the FTA are also proved. Finally, two examples are given to illustrate the feasibility and effectiveness of the proposed approach.

关键词： Fault detection and identification Predictive control Iterative learning control

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Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

Challenges of the application of data-driven models for the ...

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European control Conference

作者： Dionysios P. Xenos Olaf Kahrs Matteo Cicciotti Fernando Moreno Leira Nina F. Thornhill Department of Chemical Engineering Centre for Process Systems Engineering Imperial College London London SW7 2AZ UK Corporate Operational Excellence - Technical Process Optimisation BASF SE 67056 Ludwigshafen Germany Process Control Task Force BASF SE 67056 Ludwigshafen Germany Automation Technology - Advanced Process Control BASF SE 67056 Ludwigshafen Germany

ISBN: (纸本)9781509025923

The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.

关键词： Challenges application data-driven models

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Unit-level modelling for KPI of batch hot strip mill process using dynamic partial least squares

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IFAC-PapersOnLine 2015年第28期48卷 1005-1010页

作者： Zhang, Kai Shardt, Yuri A. W. Chen, Zhiwen Ding, Steven X. Peng, Kaixiang Institute for Automatic Control and Complex Systems University of Duisburg-Essen Bismarckstrasse 81 BB Duisburg47057 Germany Key Laboratory for Advanced Control of Iron and Steel Process School of Automation and Electrical Engineering University of Science and Technology of Beijing Beijing100083 China

This paper proposes two different approaches to the problem of modelling the key performance indicators (KPIs) in a hot strip mill process (HSMP). The first approach takes into consideration the inherent dynamics of the process and develops a nonlinear iterative dynamic partial least squares (PLS) model. The second approach takes into consideration the multibatch property of HSMP to develop an appropriate multibatch modelling framework. Both approaches can be integrated into the dynamic model framework. The proposed framework is examined using industrial data obtained from a steel plant, where it provides a decent model for this process. However, there exists the potential for improvements including dealing with interbatch variability. © 2015, IF AC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Strip mills Benchmarking Least squares approximations Steelmaking Strip metal Waste disposal Hot strip mills Industrial datum Key performance indicators Model framework Modelling framework Multibatch Partial least square (PLS) Partial least squares models

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Radar Data processing of Blast Furnace Stock-Line Based on Spatio-Temporal Data Association

Radar Data Processing of Blast Furnace Stock-Line Based on S...

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第三十四届中国控制会议

作者： Hou Qingwen Chen Xianzhong Chen Ping School of Automation & Electrical Engineering University of Science and TechnologyBeijing Key Laboratory of Advanced Control for Iron and Steel Process

ISBN: (纸本)9781467374439

A method of spatio-temporal data association processing based on FMCW radar in blast furnace was proposed in this paper. First, model-categories of basic stock-line profile through mining information of process of production are established. Next, predicting measured value on the basis of historical observations and associating with model category of basic stock-line shape-changing using grey relation algorithm, which acquires the expected distribution values. Finally, Welch FFT and peak-searching method of FT continuous spectrum are researched in the calculation of center distance. Simulation results indicate that the proposed method reduces the average rate of outliers from 13% to 4%, and the matching rate of fitting stock-line associating with prior model class library increases from 68% to 87%, compared with traditional interpolation fitting of direct measurement, which guides the operation of blast furnace charging reasonably.

关键词： FMCW radar,stock-line measurement,data association,prior information

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Radar data processing of blast furnace stock-line based on spatio-temporal data association 34

Radar data processing of blast furnace stock-line based on s...

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34th Chinese control Conference, CCC 2015

作者： Qingwen, Hou Xianzhong, Chen Ping, Chen School of Automation and Electrical Engineering University of Science and Technology Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Beijing China

ISBN: (纸本)9789881563897

关键词： data association FMCW radar prior information stock-line measurement

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Data-driven PID controller Design for Continuous Stirred Tank Reactor 34

Data-driven PID Controller Design for Continuous Stirred Tan...

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第三十四届中国控制会议

作者： XU Dezhi LIU Fei ZHOU Hongcheng ZHANG Qiang Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Institute of Information JinLing Institute of Technology School of Electrical Engineering University of Jinan

ISBN: (纸本)9789881563897

Since most chemical processes exhibit severe nonlinear and time-varying behavior, the control of such processes is challenging. In this paper, we propose data-driven controller design method based on lazy learning for chemical processes. Using a lazy learning algorithm, a local valid linear model denoting the current state of system is automatically exacted for adjusting the PID controller parameters based on input/output data. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties. The simulation results on the dynamic model of Continuous Stirred Tank Reactor(CSTR) are provided to demonstrate the effectiveness of the proposed new control techniques.

关键词： PID,Data-driven,Lazy learning,Continuous Stirred Tank Reactor(CSTR)

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Re. ``Discrete Representation of Non-dominated Sets in Multi-objective Linear Programming'' [European Journal of Operational Research 255 (2016) 687–698]

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European Journal of Operational Research 2017年第3期260卷 828-828页

作者： Lizhen Shao Matthias Ehrgott Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 10 0 083 China Department of Management Science Lancaster University Management School Bailrigg Lancaster LA1 4YX United Kingdom

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Fuzzy Inference Algorithm based on Quantitative Association Rules

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Procedia Computer Science 2015年 61卷 388-394页

作者： Ling Wang Ji-Yuan Dong Shu-Lin Li School of Automation & Electrical Engineering University of Science and Technology Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing 100083 China

In order to develop a data mining system to extract the fuzzy inference rules from the data, in this paper a fuzzy inference algorithm based on quantitative association rule (FI-QAR) is proposed. First, a discretization algorithm based on an improved clustering for each dimension data is adopted, and then the quantitative results are represented in the form of a Nominal variables matrix to compute the support and confidence level in the Apriori algorithm for quantitative association rules mining. On the basis of this, the quantitative association fuzzy rules are reconstructed by combing with TS fuzzy model to realize fuzzy inference, which can be applied to predict the output class and precise output. Experiment results demonstrated that the proposed algorithm is feasible and practical.

关键词： quantitative association rules apriori algorithm discretization TS fuzzy rules

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