Synthesis of a mobile robot spatial stabilization system based on machine learning control by symbolic regression

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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.

About the authors

Askhat I. Diveev

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Author for correspondence.
SPIN-code: 5726-6572
44/2 Vavilova St, Moscow, 119333, Russian Federation

Chief Researcher, Doctor of Technical Sciences, Professor

Neder Jair Mendez Florez

Peoples’ Friendship University of Russia (RUDN University)

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Graduate Student at the Department of Mechanics and Mechatronics, Engineering Academy


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Copyright (c) 2021 Diveev A.I., Mendez Florez N.J.

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