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作者： Matušů, Radek Prokop, Roman Matejičková, Katarína Bakošová, Monika Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 76005 Zlín Czech Republic Department of Information Engineering and Process Control Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinského 9 81237 Bratislava Slovakia

ISBN: (纸本)9789604741991

The contribution deals with design of continuous-time robustly stabilizing PI controllers for interval systems using the combination of Kronecker summation method, sixteen plant theorem and an algebraic approach to controller tuning. The effectiveness and practical applicability of the proposed method is demonstrated in control of a 3rd order nonlinear electronic plant.

关键词： Continuous time systems

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NCO-GMC cascade control algorithm based on-line optimization on fed batch fermentation

NCO-GMC cascade control algorithm based on-line optimization...

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World Congress on Intelligent control and automation (WCICA)

作者： Wei Wang Haiqing Wang Zhihuan Song Institute of Industrial Process Control University of Zhejiang Hangzhou Zhejiang China College of Electronic Engineering and Automation Changshu Institute of Technology Changsha Jiangsu China

On-line optimization and control of fed-batch fermentation is difficult due to its non linearity and dynamic operation points with uncertainty. A novel cascade optimal control algorithm was proposed to treat these difficulties with the operating points, which maximize the objective function, was defined by Necessary Conditions of Optimization (NCO). Consequently, a nonlinear Generic Model control (GMC) was applied to eliminate effect of uncertainty. Calculations of Hamilton gradient online and repeated optimization were avoided. The simulation on saccharomyces cerevisiae enzyme cultivation process showed the efficiency and advantage of the proposed method.

关键词： Optimization Silicon compounds Uncertainty Sugar process control Feeds

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Hailstone detection based on time series association rules

Hailstone detection based on time series association rules

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhiying Lu Huan Zhang Hongmin Ma Huizhen Jia School of Electrical Engineering and Automation Tianjin Key Laboratory of Process Measurement and control Tianjin University 300072 China Tianjin Meteorological Bureau 300074 China

Hailstone is one of main meteorological disasters and it is difficult to forecast effectively. In this paper, a new hail echo detection method based on time series association rules was proposed and a hail echo automatic detection system was built. Compared to the existing methods, the test results have shown that this method eliminated hail alert failure completely and also reduced the proportion of false alarm, raising the accuracy of hail forecast. The system was able to assist forecasters to make correct hail forecasts and provide reference to other disastrous weather forecast.

关键词： Time series analysis Association rules Weather forecasting Databases Radar imaging

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Estimation of component concentrations of sodium aluminate solution via PLS and Hammerstein recurrent neural networks

Estimation of component concentrations of sodium aluminate s...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Wang Lijie Zhao Tianyou Chai Wen Yu Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University China Key Laboratory of Integrated Automation of Process Industry Shenyang Institute of Chemical Technology Information Engineering School Ministry of Education China Key Laboratory of Integrated Automation of Process Industry Ministry of Education and Research Center of Automation Northeastern University China Departamento de Control Automatico CINVESTAV Mexicali Mexico

In this paper, a new on-line soft sensing method is proposed for component concentrations of sodium aluminate solution. With this sensing strategy, real-time control and optimization can be realized in aluminate production plants. Several advance techniques are used, such as PLS (Partial Least Squares), Hammerstein model, recurrent neural networks and least square algorithm. Industrial experiment results show that the proposed soft sensing algorithm is effective.

关键词： Nonlinear dynamical systems Recurrent neural networks Computational modeling Heuristic algorithms Temperature measurement Data models Artificial neural networks

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Fault Detection Filter Design for Linear Polytopic Uncertain Continuous-time Systems

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自动化学报 2010年第5期36卷 742-750页

作者： WANG Heng JU He-Hua YANG Guang-Hong College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100124 P.R. China Key Laboratory of Integrated Automation for the Process Industry Ministry of Education Shenyang 110004 P. R. China College of Information Science and Engineering Northeastern University Shenyang 110004 P. R. China

The paper studies the problem of fault detection filter design for uncertain linear continuous-time systems.A design procedure dealing with parameter uncertainties is proposed for residual generation,the sensitivity to fault and the robustness against disturbances are both enhanced on residual outputs through satisfying some performance *** the aid of the generalized Kalman-Yakubovich-Popov(GKYP)lemma,the fault sensitivity performance index can be dealt with in the given frequency range directly,which avoids approximations associated with frequency weights of the existing *** iterative algorithm based on linear matrix inequality(LMI)is given to obtain the solutions.A numerical example is given to illustrate the effectiveness of the proposed methods.

关键词：不连续系统 LMI GKYP 自动化系统

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THD reduction with grid synchronized inverter’s power injection of renewable sources

THD reduction with grid synchronized inverter’s power injec...

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International Symposium on Power Electronics, Electrical Drives, automation and Motion, SPEEDAM

作者： Péter Görbe Attila Magyar Katalin M. Hangos Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Process Control Research Group Computer and Automation Research Institute Budapest Hungary

A control method for small domestic power plants using renewable energy is described in this paper. This method is not only capable of optimizing the working point of the plant but also implements active power factor correction and lowers the extant harmonic distortion in the line. The novel element in the proposed complex controller structure is the upper harmonic controller that minimizes the amplitudes of the 3rd, 5th 7th, 9th, and the 11th upper harmonic component in the output voltage. The proposed controller has been investigated by simulation in Matlab environment, and as a result, substantial improvement of the output voltage and current waveform could be achieved. Robustness of the method against nonlinear loads has also been tested.

关键词： Power factor correction Costs Power generation Voltage control Harmonic distortion Robustness Nonlinear distortion Solar power generation Power supplies Inverters

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Soft sensor modeling of ball mill load via principal component analysis and support vector machines

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7th International Symposium on Neural Networks, ISNN 2010

作者： Tang, Jian Zhao, Lijie Yu, Wen Yue, Heng Chai, Tianyou Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110819 China College of Information Engineering Shenyang Institute of Chemical Technology Shenyang 110142 China Departamento de Control Automatico CINVESTAV-IPN Av.IPN 2508 México D.F. 07360 Mexico

ISBN: (纸本)9783642129896

In wet ball mill, measurement accuracy of mill load (ML) is very important. It affects production capacity and energy efficiency. A soft sensor method is proposed to estimate the mill load in this paper. Vibration signal of mill shell in time domain is first transformed into power spectral density (PSD) using fast Fourier transform (FFT), such that the relative amplitudes of different frequencies contain mill load information directly. Feature variables at low, medium and high frequency bands are extracted through principal component analysis (PCA), which selects input as a preprocessing procedure to improve the modeling performance. Three support vector machine (SVM) models are built to predict the mill operating parameters. A case study shows that proposed soft sensor method has higher accuracy and better predictive performance than the other normal approaches. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Principal component analysis

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PI controlLER DESIGN FOR TIME DELAY SYSTEMS

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IFAC Proceedings Volumes 2010年第2期43卷 349-354页

作者： M. BakoŜová K. Vaneková J. Závacká Department of Information Engineering and Process Control Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinského 9 812 37 Bratislava Slovak Republic fax: +421 2 52496469

The paper presents an approach to PI controller design for systems with varying time delay, which can be interpreted as systems with interval parametric uncertainty. The transfer function of the controlled system is modified by approximation of the time delay term by its Pade expansion or Taylor expansion in the numerator. PI controllers, that are able to assure robust stability of the feedback closed loop with the modified controlled system are found by the graphical method. Then the pole-placement method is used to specify those controller parameters, which assure certain quality of the control performance. Designed PI controllers are implemented using PLC SIMATIC S7-300 and tested by experiments on a laboratory electronic equipment with varying time delay.

关键词： PI controller pole-placement method robust control time-delay system

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Improving transient response of adaptive control systems using multiple neural network models

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7th International Symposium on Neural Networks, ISNN 2010

作者： Li, Xiao-Li Yu, Xiang Zhang, Yan Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing Beijing 10083 China Graduate School of Education University of Pennsylvania 3451 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9783642129896

Multiple Radial Basis Function (RBF) neural network models are used to approximate a kind of discrete time nonlinear system described by a combination of nonlinear and linear part. Based on these models, Multiple model adaptive control strategy is given to improve the transient response of closed-loop system. Simulations according to different uncertain changing parameters in the model of the system are studied to show the efficiency of the proposed method. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Transient analysis

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Modelling of mill load for wet ball mill via GA and SVM based on spectral feature

Modelling of mill load for wet ball mill via GA and SVM base...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Zhao, Lijie Tang, Jian Yu, Wen Yue, Heng Chai, Tianyou College of Information Engineering Shenyang University of Chemical Technology Shenyang I10142 China Key Laboratory of Integrated Automation of Process Industry Northeastern University Ministry of Education Shen yang Liaoning China Departamento de Control Automatico CINVESTAV-IPN Av.IPN 2508 México D.F. 07360 Mexico

ISBN: (纸本)9781424464388

The load of wet ball mill is a key parameter for grinding process, which affects the productivity, quality and energy consumption. A new soft sensor approach based on the mill shell vibration signal is proposed in this paper. As the frequency domain signal contains more evidently information than time domain, the power spectral density (PSD) of the vibration signal was obtained via fast Fourier transform (FFT). And then the mass and the central frequency of the small peaks of the spectrum are extracted as the spectral features. At last the support vector machines (SVM) is used to build the soft model. The parameters of SVM, the input variables including the mass and the central frequency of the peaks are selected by Genetic algorithm (GA). Experimental results show that proposed soft sensor model has higher accuracy and better predictive performance than the other normal approaches. © 2010 IEEE.

关键词： Genetic algorithms

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