Sensorless speed and armature resistance and temperature estimator for BDCM based on a CFNN and BFGS

What is it about?

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, or estimators which do not estimate the speed, temperature and resistance simultaneously. The proposed method has been verified both by simulation and by comparison with the simulation results available in the literature

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Photo by Charles on Unsplash

Photo by Charles on Unsplash

Why is it important?

To the authors’ knowledge, very few publications deal with the simultaneous estimation of speed and armature temperature of DC machines, especially when performed by intelligent techniques. Artificial neural networks (ANNs) have demonstrated their ability in a wide variety of applications such as process control , identification, diagnostics, pattern recognition, robot vision, flight scheduling, finance and economics, and medical diagnosis. In this paper, while referring to our previous study, in which an estimator based on a multilayer perceptron with Levenberg–Marquardt BP was developed in order to avoid the limitations of the standard ANN, a solution based on a cascade-forward neural network (CFNN) and Bayesian regulation BP (BRBP) is proposed. A highly accurate BRBP-based ANN was proposed in [38,39] but it requires an extremely long convergence time and is in fact known to be among the slowest algorithms to converge. Based on the approach already presented in [29], the purpose of this paper is to propose a novel approach using a learning algorithm that is a compromise between speed and accuracy. The BFGS can respond to these two constraints.

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