Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor

Hacene Mellah,Rachid Taleb,Kamel Eddine

doi:10.14569/ijacsa.2016.070731

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(LMBP), among the limits of LMBP in the case of large number of data, so the use of MLP based on LMBP is no longer valid in our case.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.

Highlights

In [5,6,7,8] we find several methods about DC machine temperature measurement, but the problems of temperature measurement are more complicated and difficult to solve than the speed measurement problems, since, the rotor is in rotation
A new nonlinear estimation strategy is proposed in the recent paper in this field based on combining elements of the Extended Kalman Filter (EKF) with the smooth variable structure filter (SVSF) to estimate the stator winding resistance [22], in this research we find only a resistance estimation approach, the link temperature-resistance is ignored, this is the simplest estimator version
We following the instructions discussed in the past section for obtained an optimized CFNNE, training step is the most important step to create any Artificial Neural Network (ANN), our optimized CFNNE is trained after 2000 epoch at the performance 1.6e-4

Summary

Introduction

The simplest estimation method is based on the steady-state voltage equation, where the speed is written as a function of armature voltage and current; the peaks due to converter especially in the transient state affect this speed and the link resistance-temperature is ignored on the other hand, it is the two major inconvenient of this method [1]. R. Welch Jr. et all [4] discuss the temperature effects on electrical and mechanical time constants, he prove that these time constants are not constant value, in addition the motor’s electrical resistance and its back EMF are depend on temperature. The problems of armature temperature measurement are not totally resolved

Objectives

Results

Conclusion