Simulation modeling of a feed mixer control system

A.M. Tramova, N.V. Mokrova, V.S. Artemyev

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Abstract. Controlling a feed mixer as a complex electromechanical system requires simultaneous monitoring of mixing speed and loading level, as well as overload prevention and ensuring the required mixture uniformity. The operating modes are significantly influenced by variable loads, which depend on the material mass and physical-mechanical properties, system inertia, and transient processes during startup and shutdown. Changes in the object’s parameters are taken into account in real time, and control actions are adjusted by the control system.
Aim. The study aims to develop a functional diagram of an automated control system for a feed mixer electric drive and to determine optimal motor regulation parameters.
Methods. The study employs simulation modeling based on a state-space representation of the system, defining control, disturbance, and measurement channels. This model integrates an electromechanical drive subsystem, a variable load generator, and an adaptive control algorithm with speed and torque feedback.
Results. The study develops a simulation model that evaluates the time-dependent dynamics of phase currents, electromagnetic torque, and motor speed. Our findings establish how transient processes depend on load parameters and controller settings, defining the conditions for stable system operation under variable modes.
Conclusions. Simulation modeling enables predicting the electric drive behavior of the feed mixer and justifies the selection of control parameters. Furthermore, the developed model improves tuning accuracy and ensures the target mixture homogeneity under various operating conditions.

Keywords: feed mixer, electric motor, adaptive control, simulation model

For citation. Tramova A.M., Mokrova N.V., Artemyev V.S. Simulation modeling of a feed mixer control system. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 3. Pp. 107–121. DOI: 10.35330/1991-6639-2026-28-3-107-121

©   Tramova A.M., Mokrova N.V., Artemyev V.S., 2026

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Information about the authors

Aziza M. Tramova, Doctor of Economic Sciences, Associate Professor, Professor, Department of Informatics, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
TramovaAM@rea.ru, ORCID: https://orcid.org/0000-0002-4089-6580, SPIN-code: 8583-3592
Nataliya V. Mokrova, Doctor of Technical Sciences, Associate Professor, National University of Science and Technology MISIS;
4, Leninsky prospekt, building1, Moscow, 119049, Russia;
Department of Informatics, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
nvmokrova@misis.ru, ORCID: https://orcid.org/0000-0002-8444-2935, SPIN-code: 5157-9790
Viktor S. Artemyev, Senior Lecturer, Plekhanov Russian University of Economics;
36, Stremyannyy lane, Moscow, 115054, Russia;
artemev.vs@rea.ru, ORCID: https://orcid.org/0000-0002-0860-6328, SPIN-code: 8912-5825

Funding

The study was performed without external funding.