[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122054-en":3,"doc-seo-122054-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122054,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","CONTROL SYSTEMS SYNTHESIS FOR ROBOTS ON THE BASE OF MACHINE LEARNING BY SYMBOLIC REGRESSION","This paper proposes a numerical method for control system synthesis using machine learning, with symbolic regression at its core. Symbolic regression constructs mathematical expressions for control functions directly from system data, reducing manual programming and tuning typical of traditional workflows. A general machine-learning framework is presented, emphasizing evolutionary algorithms and an algorithmic strategy based on small variations from a baseline solution to improve efficiency. A mobile robot case study validates automatic control law generation with reduced development time and improved accuracy, while noting computational and real-time challenges.","128  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 19, SEPTEMBER 2024  \nDOI: 10.37943/19OXFC5347  \nAskhat Diveev  \nPhD, Associate Professor of the Department of Computer Science, Institute of Engineering and Technology  \n[aidiveev@mail.ru](aidiveev@mail.ru), [orcid.org/0000-0003-2057-9016](orcid.org/0000-0003-2057-9016)  \nKorkyt Ata Kyzylorda University, Kazakhstan  \nNurbek Konyrbaev  \nPhD, Аssociate professor, head of the Department of Computer Science, Institute of Engineering and Technology [n.konyrbaev@mail.ru](n.konyrbaev@mail.ru), [orcid.org/0000-0002-8788-4149](orcid.org/0000-0002-8788-4149)  \nKorkyt Ata Kyzylorda University, Kazakhstan  \nZharasbek Baishemirov  \nPhD, Associate Professor of Mathematics and Mathematical Modeling [zbai.kz@gmail.com](zbai.kz@gmail.com), [orcid.org/0000-0002-4812-4104](orcid.org/0000-0002-4812-4104)  \nAbai Kazakh National Pedagogical University, Kazakhstan Associate Professor of School of Applied Mathematics  \nKazakh-British Technical University, Kazakhstan  \nAsem Galymzhankyzy  \nMaster, Teacher of the Department of Computer Science, Institute of Engineering and Technology  \n[asem.galymzhankyzy@gmail.com](asem.galymzhankyzy@gmail.com), [orcid.org/0009-0004-4624-8797](orcid.org/0009-0004-4624-8797)[ ](orcid.org/0009-0004-4624-8797)Korkyt Ata Kyzylorda University, Kazakhstan  \nOralbek Abdullayev  \nMaster, Teacher of the Department of Computer Science, Institute of Engineering and Technology  \n[oralbek.abdullaev@mail.ru](oralbek.abdullaev@mail.ru), [orcid.org/0009-0001-5649-6805](orcid.org/0009-0001-5649-6805)[ ](orcid.org/0009-0001-5649-6805)Korkyt Ata Kyzylorda University, Kazakhstan  \nCONTROL SYSTEMS SYNTHESIS FOR ROBOTS ON THE BASE OF MACHINE LEARNING BY SYMBOLIC REGRESSION  \nAbstract: This paper presents a novel numerical method for solving the control system synthesis problem through the application of machine learning techniques, with a particular focus on symbolic regression. Symbolic regression is used to automate the development of control systems by constructing mathematical expressions that describe control functions based on system data. Unlike traditional methods, which often require manual programming and tuning, this approach leverages machine learning to discover optimal control solutions. The paper introduces a general framework for machine learning in control system design, with an emphasis on the use of evolutionary algorithms to optimize the generated control functions. The key contribution of this research lies in the development of an algorithm based on the principle of small variations in the baseline solution. This approach significantly enhances the efficiency of discovering optimal control functions by systematically exploring the solution space with minimal adjustments. The method allows for the automatic generation of control laws, reducing the need for manual coding, which is especially beneficial in the context of complex control systems, such as robotics. To demonstrate the applicability of the method, the research applies symbolic regression to the control synthesis of a mobile robot.  \nCopyright © 2024, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 09.09.2024 Accepted: 26.09.2024 Published: 30.09.2024  \nDOI: 10. 37943/19OXFC5347  \n© Askhat Diveev, Nurbek Konyrbaev, Zharasbek Baishemirov, Asem Galymzhankyzy, Oralbek Abdullayev  \n129  \nThe results of this case study show that symbolic regression can effectively automate the process of generating control functions, significantly reducing development time while improving accuracy. However, the paper also acknowledges certain limitations, including the computational demands required for symbolic regression and the challenges associated with real-time implementation in highly dynamic environments. These issues represent important areas for future research, where further optimization and hybrid approaches may enhance the me","cbCaiqnaPsKV0dxR","https://ap.wps.com/l/cbCaiqnaPsKV0dxR","pdf",1022892,1,12,"English","en",105,"# Introduction\n# Proposed machine learning framework\n## Symbolic regression for control functions\n## Evolutionary optimization and baseline small variations\n# Mobile robot case study\n## Results and limitations\n# Conclusion and future work","[{\"question\":\"How does symbolic regression contribute to control system synthesis in this paper?\",\"answer\":\"Symbolic regression automatically builds mathematical expressions for control functions using system data, avoiding manual coding and parameter tuning required by conventional methods.\"},{\"question\":\"What optimization strategy improves the efficiency of discovering control functions?\",\"answer\":\"The method uses evolutionary algorithms guided by an approach that explores the solution space through small variations of a baseline solution.\"},{\"question\":\"What limitations and future research directions are identified?\",\"answer\":\"The paper notes computational demands of symbolic regression and difficulties for real-time use in highly dynamic environments, proposing optimization and hybrid approaches for better practicality and scalability.\"}]","CONTROL SYSTEMS SYNTHESIS FOR ROBOTS ON THE BASE OF MACHINE LEARNING BY SYMBOLIC REGRESSION | 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