Abstract
The article deals with a problem of a speed control of a DC electric drive of a reverse rolling mill under the conditions of its mechanics parameters drift and influence of disturbances. The analysis of existing methods to solve it is made. As a result, two intelligent methods are chosen: the neural network (proposed by the authors) and the fuzzy logic based tuners of linear controllers, the efficiency of which are to be compared. The neural tuner consists of two neural networks calculating the controller parameters of the electric drive, and a rule base that determines at what moments and speed to train these networks. A general description of the fuzzy tuner is also provided. Experimental studies are made using a model of the electric drive of the rolling mill under the above mentioned conditions. The obtained results show that the neural tuner, contrary to the fuzzy one, keeps the speed overshoot within the required limits and also reduces the time of disturbance rejection by 30%.
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Acknowledgments
This work was supported by the Ministry of education and science of the Russian Federation. Grant No. 14.575.21.0133 (RFMEFI57517X0133).
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Glushchenko, A.I., Petrov, V.A. (2019). On Comparative Evaluation of Effectiveness of Neural Network and Fuzzy Logic Based Adjusters of Speed Controller for Rolling Mill Drive. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research II. NEUROINFORMATICS 2018. Studies in Computational Intelligence, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-030-01328-8_15
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DOI: https://doi.org/10.1007/978-3-030-01328-8_15
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