Extensive efforts have been conducted on the elimination of position sensors in servomotor control. The purpose of this paper is to aim at estimating the servomotor speed without using position sensors and the knowledge of its parameters by artificial neural networks (ANNs).
A neural speed observer based on the Elman neural network (NN) structure takes only motor voltages and currents as inputs.
After offline NNs training, the observer is incorporated into a DSP-based drive and sensorless control is achieved.
Future work will consider to reduce the computation time for NNs training and to adaptively tune parameters on line.
The experimental results of the proposed method are presented to show the effectiveness.
This paper achieves sensorless servomotor control by ANNs which are seldom studied.
This research is supported by the National Science Council, ROC under Contract No. NSC 100-2632-E-218-001-MY3.
Horng, J.-R., Wang, M.-S., Lai, T.-R. and Berinde, S. (2014), "A neural observer for sensorless speed control of servomotors", Engineering Computations, Vol. 31 No. 8, pp. 1668-1678. https://doi.org/10.1108/EC-11-2012-0289
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