67085

Автор(ы): 

Автор(ов): 

1

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Method of Calculation of Upper Bound of Learning Rate for Neural Tuner to Control DC Drive

Электронная публикация: 

Да

ISBN/ISSN: 

1860-949X

DOI: 

10.1007/978-3-319-66604-4_16

Наименование источника: 

  • Studies in Computational Intelligence

Обозначение и номер тома: 

Vol. 736

Город: 

  • Берлин

Издательство: 

  • Springer

Год издания: 

2018

Страницы: 

104-109
Аннотация
A two-loop cascade control system of a DC drive is considered in this research. The task is to keep transients quality in both speed and armature current loops. It is solved by a usage of P- and PI-controller parameters neural tuner, which operates in real time and does not require a plant model. The tuner is trained online during its functioning in order to follow the plant parameters change, but usage of too high values of a learning rate may result in instability of the control system. So, the upper bound of the learning rate value calculation method is proposed. It is based on Lyapunov’s second method application to estimate the system sustainability. It is applied to implement adaptive control of a mathematical model of a two-high rolling mill. Obtained results show that the proposed method is reliable. The tuner allowed to reduce the plant energy consumption by 1–2% comparing to conventional P-controller.

Библиографическая ссылка: 

Глущенко А.И. Method of Calculation of Upper Bound of Learning Rate for Neural Tuner to Control DC Drive // Studies in Computational Intelligence. 2018. Vol. 736. С. 104-109.