Experiment on Sensorless Control of an Induction Motor using Backpropagation Neural Network

Experiment on Sensorless Control of an Induction Motor using Backpropagation Neural Network
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© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Pham Thi Giang, Vo Quang Vinh, Tran Thuy Van, Vo Thanh Ha
DOI : 10.14445/22315381/IJETT-V70I10P211

How to Cite?

Pham Thi Giang, Vo Quang Vinh, Tran Thuy Van, Vo Thanh Ha, "Experiment on Sensorless Control of an Induction Motor using Backpropagation Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 87-97, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P211

Abstract
A three-phase induction motor is commonly utilized in industrial and electric vehicles. Furthermore, electric drive systems without speed sensors are becoming more popular because of their small size, cheap cost, outstanding dependability, and sustainability combined with innovative algorithms and controls. As a result, speed estimate techniques are used in these sensorless drive systems instead of direct speed measuring equipment like tachometers or photoelectric encoders. Based on an artificial neural network, this research proposes a technique for estimating the speed of a three-phase asynchronous motor without a speed sensor. The engine's speed determines the neural network's weight, and the real-time error backpropagation algorithm updates the sample period. They are using experiments with the suggested estimate approach. The simulation and experiment findings demonstrate that when the speed response error is estimated using the actual speed using an artificial neural.

Keywords
Induction Motor, Neural Network, Luenberger Observer, Kalman Filter Observer, Sliding mode Observer.

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