Title: Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor,
Abstract: The objective of this paper is to develop an Artificial Neural Network (ANN)
model to estimate simultaneously, parameters and state of a brushed DC machine.
The proposed ANN estimator is novel in the sense that his estimates
simultaneously temperature, speed and rotor resistance based only on the
measurement of the voltage and current inputs. Many types of ANN estimators
have been designed by a lot of researchers during the last two decades. Each
type is designed for a specific application. The thermal behavior of the motor
is very slow, which leads to large amounts of data sets. The standard ANN use
often Multi-Layer Perceptron (MLP) with Levenberg-Marquardt Backpropagation
(LMBP), among the limits of LMBP in the case of large number of data, so the
use of MLP based on LMBP is no longer valid in our case. As solution, we
propose the use of Cascade-Forward Neural Network (CFNN) based Bayesian
Regulation backpropagation (BRBP). To test our estimator robustness a random
white-Gaussian noise has been added to the sets. The proposed estimator is in
our viewpoint accurate and robust.