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Fuzzy Kalman filter parametric adjustment in the electrothermal reactor management system


S.V. Panchenko – Dr.Sc. (Eng.), Professor, Department of Industrial Power System, Branch of National Research University «Moscow Power Engineering Institute» in Smolensk. E-mail: A.Y. Puchkov – Ph.D. (Eng.), Associate Professor, Department of Management and Information Technologies in Economy, Branch of National Research University «Moscow Power Engineering Institute» in Smolensk. E-mail: Yu.V. Selyavskiy – Post-graduate Student, Department of Management and Information Technologies in Economy, Branch of National Research University «Moscow Power Engineering Institute» in Smolensk. E-mail:

The state of ore-smelting electrothermal phosphoric furnace is based on information, incoming from measuring equipment, errors and noises that can impair the accuracy of results, which are with he usage of the designed model, and the quality of the management process. In this connection the task of confounding factors reduction is actual, one of the approaches for solving is using of filtration algorithms in measurement channels. Frequency filters can be used for noise reduction, but if frequency spectrum of noise and wanted signal are overspilled, their approach is not advisable. Under these circumstances, the statistic filtration should be used, one of its methods is the Kalman filter. In contrast to frequency filters, additional hardware is not required, because changing introduction to an actual measuring data processing procedure. The authors report the method of forming filter parameters definition applied in fuzzy Kalman measuring equipment of electrothermal reactor management system signals estimation. The basis for the method is a forming filter adjustment, which underlying structure is considered to be known, with the usage of computer model for course processes regimes in electro-thermal reactor imitating. The process of adjustment is the iterative procedure of forming filter matrix elements changing on the ground of correlating its input with the computer model output results when their output signals are the same. The adjustment algorithm involves fuzzy logical conclusion system which analyses the discrepancy of model output signals and forming filter, then counts the correction. The results of numerical experimentation realizing the suggested method of forming filter adjustment, demonstrating the method`s operability in the programming environment MatLab are represented in the article.


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