Open AccessSynthesis of a mobile robot spatial stabilization system based on machine learning control by symbolic regression
Author(s) -
Askhat Diveev,
Дивеев Асхат Ибрагимович,
Neder Jair Mendez Florez,
Мендес Флорес Недер Хаир
Publication year - 2021
Publication title -
vestnik rossijskogo universiteta družby narodov. seriâ inženernye issledovaniâ
Language(s) - English
Resource type - Journals
eISSN - 2312-8151
pISSN - 2312-8143
DOI - 10.22363/2312-8143-2021-22-2-129-138
Subject(s) - symbolic regression , computer science , problem statement , operator (biology) , field (mathematics) , mobile robot , artificial intelligence , optimal control , robot , genetic programming , mathematical optimization , mathematics , biochemistry , chemistry , repressor , management science , transcription factor , pure mathematics , economics , gene
The spatial stabilization system synthesis problem of the robot is considered. The historical overview of methods and approaches for solving the problem of control synthesis is given. It is shown that the control synthesis problem is the most important task in the field of control, for which there are no universal numerical methods for solving it. As one of the ways to solve this problem, it is proposed to use the method of machine learning based on the application of modern symbolic regression methods. This allows you to build universal algorithms for solving control synthesis problems. Several most promising symbolic regression methods are considered for application in control tasks. The formal statement of the control synthesis problem for its numerical solution is given. Examples of solving problems of synthesis of system of spatial stabilization of mobile robot by method of network operator and variation Cartesian genetic programming are given. The problem required finding one nonlinear feedback function to move the robot from thirty initial conditions to one terminal point. Mathematical records of the obtained control functions are given. Results of simulation of control systems obtained by symbolic regression methods are given.