Machine Learning Control Based on Approximation of Optimal Trajectories

Askhat Diveev, Sergey Konstantinov, Elizaveta Shmalko and Ge Dong
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Askhat Diveev: Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia
Sergey Konstantinov: Department of Mechanics and Mechatronics, RUDN University, 117198 Moscow, Russia
Elizaveta Shmalko: Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia
Ge Dong: School of Aerospace Engineering, Tsinghua University, Beijing 100084, China

Mathematics, 2021, vol. 9, issue 3, 1-17

Abstract: 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.

Keywords: machine learning control; general synthesis problem; symbolic regression; optimal control; evolutionary algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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