Synthesis of an intelligent control algorithm based on a digital twin and neural network approximation

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Abstract

Background. This paper examines the problem of synthesizing an adaptive-predictive algorithm for selecting control modes for electric heating of turnouts.

Aim: Synthesis of an adaptive-predictive algorithm that ensures improved control efficiency.

Methods: Development of an integrated risk assessment that takes into account the state of the digital twin of the object, forecasted external impacts, sensor data, and equipment readiness, using neural network approximation for parameter synthesis.

Results: The algorithm structure, the set of adjustable parameters, and an ensemble of climate scenarios for simulation-optimization tuning have been developed.

Conclusion: The method enables adaptive selection of control modes while meeting safety and reliability requirements.

About the authors

Viktor A. Barausov

OOO GC IMSAT

Author for correspondence.
Email: barausovv@gmail.com
ORCID iD: 0000-0002-8106-1192
SPIN-code: 3742-3909

Project Manager

Russian Federation, St. Petersburg

Vladimir P. Bubnov

Emperor Alexander I Petersburg State Transport University

Email: bubnov1950@yandex.ru
ORCID iD: 0000-0002-6742-3011
SPIN-code: 3114-6579

Dr. Sci. (Engineering), Professor

Russian Federation, St. Petersburg

Vyacheslav S. Zabuzov

A.F. Mozhaisky Military Space Academy

Email: zzvvss80@mail.ru
ORCID iD: 0009-0008-6100-2145
SPIN-code: 5112-6858

Cand. Sci. (Engineering), Associate Professor

Russian Federation, St. Petersburg

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