67090

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

On Comparative Evaluation of Effectiveness of Neural Network and Fuzzy Logic Based Adjusters of Speed Controller for Rolling Mill Drive

ISBN/ISSN: 

1860-949X

DOI: 

10.1007/978-3-030-01328-8_15

Наименование источника: 

  • Studies in Computational Intelligence

Обозначение и номер тома: 

Vol. 799

Город: 

  • Берлинед

Издательство: 

  • Springer

Год издания: 

2019

Страницы: 

144-150
Аннотация
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%.

Библиографическая ссылка: 

Глущенко А.И., Петров В.А. On Comparative Evaluation of Effectiveness of Neural Network and Fuzzy Logic Based Adjusters of Speed Controller for Rolling Mill Drive // Studies in Computational Intelligence. 2019. Vol. 799. С. 144-150.