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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">RUDN Journal of Engineering Research</journal-id><journal-title-group><journal-title xml:lang="en">RUDN Journal of Engineering Research</journal-title><trans-title-group xml:lang="ru"><trans-title>Вестник Российского университета дружбы народов. Серия: Инженерные исследования</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2312-8143</issn><issn publication-format="electronic">2312-8151</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">27545</article-id><article-id pub-id-type="doi">10.22363/2312-8143-2021-22-2-129-138</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Synthesis of a mobile robot spatial stabilization system based on machine learning control by symbolic regression</article-title><trans-title-group xml:lang="ru"><trans-title>Синтез системы пространственной стабилизации мобильного робота на основе обучения методом символьной регрессии</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="spin">5726-6572</contrib-id><name-alternatives><name xml:lang="en"><surname>Diveev</surname><given-names>Askhat I.</given-names></name><name xml:lang="ru"><surname>Дивеев</surname><given-names>Асхат Ибрагимович</given-names></name></name-alternatives><bio xml:lang="en"><p>Chief Researcher, Doctor of Technical Sciences, Professor</p></bio><bio xml:lang="ru"><p>главный научный сотрудник, доктор технических наук, профессор</p></bio><email>aidiveev@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Mendez Florez</surname><given-names>Neder Jair</given-names></name><name xml:lang="ru"><surname>Мендес Флорес</surname><given-names>Недер Хаир</given-names></name></name-alternatives><bio xml:lang="en"><p>Graduate Student at the Department of Mechanics and Mechatronics, Engineering Academy</p></bio><bio xml:lang="ru"><p>аспирант департамента механики и мехатроники , инженерная академия</p></bio><email>nederjair@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences</institution></aff><aff><institution xml:lang="ru">Федеральный исследовательский центр «Информатика и управление» Российской академии наук</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Peoples’ Friendship University of Russia (RUDN University)</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2021-10-02" publication-format="electronic"><day>02</day><month>10</month><year>2021</year></pub-date><volume>22</volume><issue>2</issue><issue-title xml:lang="en">VOL 22, NO2 (2021)</issue-title><issue-title xml:lang="ru">ТОМ 22, №2 (2021)</issue-title><fpage>129</fpage><lpage>138</lpage><history><date date-type="received" iso-8601-date="2021-10-02"><day>02</day><month>10</month><year>2021</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2021, Diveev A.I., Mendez Florez N.J.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2021, Дивеев А.И., Мендес Флорес Н.Х.</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="en">Diveev A.I., Mendez Florez N.J.</copyright-holder><copyright-holder xml:lang="ru">Дивеев А.И., Мендес Флорес Н.Х.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">http://creativecommons.org/licenses/by/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/engineering-researches/article/view/27545">https://journals.rudn.ru/engineering-researches/article/view/27545</self-uri><abstract xml:lang="en"><p style="text-align: justify;">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.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">Рассматривается задача синтеза системы пространственной стабилизации робота. Приведен исторический обзор методов и подходов решения задачи синтеза управления. Показано, что задача синтеза системы управления является важнейшей задачей в области управления, для которой не существует универсальных численных методов ее решения. В качестве одного из путей решения данной проблемы предложено использовать методы машинного обучения на основе применения современных методов символьной регрессии. Для автоматического решения задачи предлагается использовать обучение системы управления методами символьной регрессии. Это позволяет построить универсальные алгоритмы решения задач синтеза управления. Рассмотрено несколько наиболее перспективных для применения в задачах управления методов символьной регрессии. Приведена формальная постановка задачи синтеза управления для ее численного решения. Приведены примеры решения задач синтеза системы пространственной стабилизации мобильного робота методом сетевого оператора и вариационного декартова генетического программирования. В задаче требовалось найти одну нелинейную функцию обратной связи, чтобы переместить робот из тридцати начальных условий в одну терминальную точку. Представлены результаты моделирования, полученные методами символьной регрессии систем управления.</p></trans-abstract><kwd-group xml:lang="en"><kwd>synthesis of control</kwd><kwd>machine learning control</kwd><kwd>symbolic regression</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>синтез управления</kwd><kwd>машинное обучение управления</kwd><kwd>символьная регрессия</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">Bellman R, Glickberg I, Gross O. Some Aspects of the Mathematical Theory of Control Processes. Rand Corporation. Santa Monica, California; 1958.</mixed-citation><mixed-citation xml:lang="ru">Bellman R., Glickberg I., Gross O. Some Aspects of the Mathematical Theory of Control Processes. Rand Corporation. 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