Helicopters Turboshaft Engines Parameters Identification Using Neural Network Technologies Based on the Kalman Filter

EasyChair Preprint 10679

Abstract

The work is a continuation of the research devoted to the development of a multidimensional Kalman filter connected at the output of the built-in helicopter’s turboshaft engines mathematical dynamic model to improve the accuracy of helicopters turboshaft engines parameters identification and achieve high quality automatic control. The main difference is the use of radial basis functions neural networks, in which the multivariate Kalman filter is a training algorithm. The work illustrates well-known mathematical expressions underlying of optimal multidimensional filtering algorithms. The methods of mathematical modeling in the Matlab environment tested the proposed algorithms. The simulation results showed that the use of neural networks trained by the multidimensional Kalman matrix filter as part of the model of helicopters turboshaft engines built into the automatic control system allows achieving high indicators of the accuracy of identifying the parameters of helicopters turboshaft engines automatic control system – up to 0.9975, then in practical analogues

Keyphrases: Accuracy, Helicopter Turboshaft Engine, Kalman filter, neural network, training

@booklet{EasyChair:10679,
  author    = {Serhii Vladov and Yurii Shmelov and Ruslan Yakovliev and Maryna Petchenko},
  title     = {Helicopters Turboshaft Engines Parameters Identification Using Neural Network Technologies Based on the Kalman Filter},
  howpublished = {EasyChair Preprint 10679},
  year      = {EasyChair, 2023}}