Estimation of speed, armature temperature and resistance in brushed DC machines using a CFNN based on BFGS BP

TL;DR

This paper introduces a sensorless estimator for brushed DC machines that uses a CFNN trained with BFGS BP to simultaneously estimate speed, armature temperature, and resistance without thermal sensors.

Contribution

It presents a novel neural network-based estimator that estimates multiple parameters simultaneously, avoiding reliance on thermal sensors and improving over existing methods.

Findings

Accurately estimates speed, temperature, and resistance in simulations.

Outperforms traditional non-intelligent estimators like EKF.

Validated through simulation comparisons.

Abstract

In this paper, a sensorless speed and armature resistance and temperature estimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward Neural Network (CFNN) and Quasi-Newton BFGS backpropagation (BP). Since we wish to avoid the use of a thermal sensor, a thermal model is needed to estimate the temperature of the BDC machine. Previous studies propose either non-intelligent estimators which depend on the model, such as the Extended Kalman Filter (EKF) and Luenberger's observer, or estimators which do not estimate the speed, temperature and resistance simultaneously. The proposed method has been verified both by simulation and by comparison with the simulation results available in the literature