Open AccessSynthesized optimal control based on machine learning
Author(s) -
Askhat Diveev,
Elizaveta Shmalko
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1727/1/012006
Subject(s) - position (finance) , symbolic regression , computer science , control theory (sociology) , control (management) , stability (learning theory) , point (geometry) , equilibrium point , object (grammar) , state (computer science) , control point , artificial intelligence , control engineering , machine learning , algorithm , mathematics , engineering , geometry , mathematical analysis , finance , economics , genetic programming , differential equation
The article discusses symbolic regression methods as a machine learning technology. The technique is tested on a complex problem of control systems synthesis. A new type of control based on changing the position of a stable equilibrium point is proposed. The implementation of such control requires the construction of a double feedback loop. The inner contour ensures the stability of the control object relative to some point in the state space. The outer contour provides optimal control of the stable equilibrium point position. To implement control, symbolic regression methods are used as machine learning technologies. It is shown that such a control is the least sensitive to external disturbances and model uncertainties.