MACHINE LEARNING BASED ON SYMBOLIC REGRESSION FOR AUTOMATED SYNTHESIS OF UNIVERSAL MOTION STABILIZATION SYSTEMS
- 作者: Diveev A.I.1,2, Barabash A.D.1
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隶属关系:
- Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences
- People's Friendship University of Russia
- 期: 编号 3 (2025)
- 页面: 5-16
- 栏目: FUNDAMENTALS OF RELIABILITY AND QUALITY ISSUES
- URL: https://journal-vniispk.ru/2307-4205/article/view/353660
- DOI: https://doi.org/10.21685/2307-4205-2025-3-1
- ID: 353660
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Background. This study considers an optimal control problem in an extended formulation, aimed at ensuring the feasibility of the solution under real-world operating conditions. Materials and methods. To achieve high accuracy and stability of the controlled plant's motion, it is proposed to synthesize a universal stabilization system capable of reliably following a wide range of trajectories, even in the presence of external influences and model uncertainties. The concept of an extended model of the controlled plant is presented, including both the plant itself with the stabilization system and a reference model for generating the optimal trajectory. It is shown that using such a structure, classical optimal control methods can be applied to obtain the control function as a function of time, achieving a high degree of conformity between the plant's motion and the planned trajectory while maintaining stability and control accuracy. To synthesize the universal stabilization system, machine learning based on symbolic regression is used, which allows for the formalization of the process of constructing control functions and eliminates the subjective errors typical of manual design. Results and conclusions. The effectiveness of the proposed approach is confirmed by a computational example of controlling the spatial motion of a group of quadcopters – a typical example of complex engineering systems with high requirements for reliability, maneuverability, and safety.
作者简介
Askhat Diveev
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences; People's Friendship University of Russia
编辑信件的主要联系方式.
Email: aidiveev@mail.ru
Doctor of technical sciences, professor, chief researcher, head of department 55, Director of the Robot Center; professor of the department of mechanics and mechatronics
40 Vavilov street, Moscow, Russia; 3 Ordzhonikidze street, Moscow, RussiaArtem Barabash
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences
Email: artew44@gmail.com
Postgraduate student
40 Vavilov street, Moscow, Russia参考
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