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

@article{Mellah2018EstimationOS,
  title={Estimation of speed, armature temperature, and resistance in brushed DC machines using a CFNN based on BFGS BP},
  author={Hacene Mellah and Kamel Eddine Hemsas and Rachid Taleb and Carlo Cecati},
  journal={Turkish J. Electr. Eng. Comput. Sci.},
  year={2018},
  volume={26},
  pages={3182-3192}
}
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… 

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