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

TL;DR

This paper presents a novel Bayesian neural network approach for real-time estimation of temperature, speed, and rotor resistance in a brushed DC motor using only voltage and current measurements, demonstrating robustness to noise.

Contribution

It introduces a Bayesian regulation-based cascade-forward neural network for simultaneous parameter and state estimation in brushed DC motors, addressing data size and noise robustness issues.

Findings

The proposed estimator accurately estimates motor parameters and states.

It remains robust under noisy measurement conditions.

The method outperforms traditional neural network approaches.

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(LMBP), among the limits of LMBP in the case of large number of data, so the use of MLP based on LMBP is no longer valid in our case.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