Machine learning control based on approximation of optimal trajectories

The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Авторы
Diveev A. 1 , Konstantinov S. 2 , Shmalko E. 1 , Dong G.3
Журнал
Издательство
MDPI AG
Номер выпуска
3
Язык
Английский
Страницы
1-17
Статус
Опубликовано
Номер
265
Том
9
Год
2021
Организации
  • 1 Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, 119333, Russian Federation
  • 2 Department of Mechanics and Mechatronics, RUDN University, Moscow, 117198, Russian Federation
  • 3 School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
Ключевые слова
Evolutionary algorithm; General synthesis problem; Machine learning control; Optimal control; Symbolic regression
Дата создания
20.04.2021
Дата изменения
20.04.2021
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/71929/