ADRC Load Side Speed Controller Parameters Adjustment Based on a Neural Model Applied for a Nonlinear Two-Mass Drive System

The paper is focused on improvements to the conventional speed controller based on Active Disturbance Rejection Control (ADRC) applied for a two-mass electric drive system. The described ADRC structure is based on load-side speed measurement. The paper compares the base structure dynamics with the overall system behavior when plant parameters are changed. The proposed ADRC algorithm extension performs soft controller parameters adjustment to improve the dynamics and plant response. The presented approach accomplishes adaptation capabilities with the use of a Radial Function Neural Network (RBFNN). The article compares the dynamic response of the plant controlled by the conventional ADRC algorithm and the designed neural adaptation extension through the conducted experimental tests.

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Publication Details

Published
2025-05-14
DOI
https://doi.org/10.18690/um.feri.4.2025.7
Primary Topic
Control Systems in Engineering
Type
article
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article

ADRC Load Side Speed Controller Parameters Adjustment Based on a Neural Model Applied for a Nonlinear Two-Mass Drive System

Marcin Kamiński, Radosław Stanisławski, Łukasz Knypiński, Danton Diego Ferreira et al.
Control Systems in Engineering
article

ADRC Load Side Speed Controller Parameters Adjustment Based on a Neural Model Applied for a Nonlinear Two-Mass Drive System

Marcin Kamiński, Radosław Stanisławski, Łukasz Knypiński, Danton Diego Ferreira, G. Kaczmarczyk
article en

Abstract

The paper is focused on improvements to the conventional speed controller based on Active Disturbance Rejection Control (ADRC) applied for a two-mass electric drive system. The described ADRC structure is based on load-side speed measurement. The paper compares the base structure dynamics with the overall system behavior when plant parameters are changed. The proposed ADRC algorithm extension performs soft controller parameters adjustment to improve the dynamics and plant response. The presented approach accomplishes adaptation capabilities with the use of a Radial Function Neural Network (RBFNN). The article compares the dynamic response of the plant controlled by the conventional ADRC algorithm and the designed neural adaptation extension through the conducted experimental tests.

Elekta (Switzerland) (CH), Centro Universitário de Lavras (BR), Elektro Ljubljana (Slovenia) (SI)
Openalex Percentile: Top 90%
Control Systems in Engineering
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