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

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 and quasi-Newton BFGS backpropagation. 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 nonintelligent estimators that depend on the model, such as the extended Kalman filter and Luenberger’s observer, or estimators that 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.