Article

Received 28.07.2025

Revised 25.11.2025

Accepted 29.12.2025

Retrieved from Vol. 28, No. 2, 2025

Pages 128 -139

Zalata, A. (2025). Methodology for training a neuro-fuzzy control system for a diesel-generator unit under variableoperating conditions. Transport Systems and Technologies, 28(2), 128-139. https://doi.org/10.32703/2617-9040-2025-46-9

Methodology for training a neuro-fuzzy control system for a diesel-generator unit under variableoperating conditions

Andrii Zalata

This paper presents a methodology for constructing and training a neuro-fuzzy control system for a diesel-generator  unit  operating  under  variable  railway  conditions.  Modern  traction  power  units encounter significant fluctuations in operational factors such as train mass, track profile, and section length, which necessitate adaptive regulation of power output. Traditional control systems are limited in  their  ability  to  respond  to  complex  multifactor  dynamics,  motivating  the  use  of  hybrid  intelligent systems. The proposed approach integrates Fuzzy C-Means (FCM) clustering to determine the initial structure of the fuzzy rule base and to form Gaussian membership functions based on cluster centers. A hybrid learning strategy is implemented, combining backpropagation and stochastic gradient descent to  adjust  both  the  fuzzy  and  neural  components  of the  model.  This  enables  the  system  to  refine membership parameters, optimize rule interactions, and adapt to nonlinearities in the operational data.The developed neuro-fuzzy model is validated using test samples not included in the training dataset. The  results  demonstrate  high  approximation  accuracy  and  strong  generalization  capability,  with prediction errors remaining within acceptable limits. The model effectively reproduces optimal control actions across diverse operating scenarios. The proposed methodology is suitable for integration into traction energy control systems and provides a foundation for future enhancements through expanded datasets, improved optimization algorithms, and full-scale simulation or field testing

diesel generator set; intelligent control; neuro-fuzzy systems; machine learning; autonomous rolling stock
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https://doi.org/10.32703/2617-9040-2025-46-9