  A sensorless speed, average temperature and resistance estimation technique
based on Neural Network (NN) for brushed DC machines is proposed in this paper.
The literature on parameters and state spaces estimations of the Brushed DC
machines, shows a variety of approaches. However, these observers are sensitive
to a noise, on the model accuracy also are difficult to stabilize and to
converge. Furthermore, the majority of earlier works, estimate either the speed
or the temperature or the winding resistance. According to the literatures, the
Resilient backpropagation (RBP) as is the known as the faster BP algorithm,
Cascade-Forward Neural Network (CFNN), is known as the among accelerated
learning backpropagation algorithms, that's why where it is found in several
researches, also in several applications in these few years. The main objective
of this paper is to introduce an intelligent sensor based on resilient BP to
estimate simultaneously the speed, armature temperature and resistance of
brushed DC machines only from the measured current and voltage. A comparison
between the obtained results and the results of traditional estimator has been
made to prove the ability of the proposed method. This method can be embedded
in thermal monitoring systems, in high performance motor drives.
