@Article{es1130,
author="Diveev, Askhat and Sofronova, Elena and Konyrbaev, Nurbek and , and Ibadulla, Sabit",
title="Stabilisation System Synthesis for Motion along the Trajectory by Evolutionary Machine Learning Control",
journal="Engineered Science",
year="",
volume="",
pages="",
abstract="The article discusses the problem of synthesising a system for stabilising the movement of an object along a given trajectory. Solving the control synthesis problem involves finding the control function on the deviation of the object from a given trajectory. A trajectory stabilisation system is necessary for the object to maintain its trajectory under real conditions in the presence of external disturbances. In the work, machine learning control by symbolic regression was used to solve the control function synthesis problem. Symbolic regression methods allow to find the mathematical expressions of the desired functions in the form of special code. To find the mathematical expression of the desired function, the symbolic regression method uses a special genetic algorithm that searches the code space for the optimal solution according to the given optimisation criterion. An example of motion stabilisation of two quadcopters along optimal trajectories is presented.",
issn="2576-9898",
doi="10.30919/es1130",
url="http://dx.doi.org/10.30919/es1130"
}