Phase flux linkage estimation of external rotor switched reluctance motor with NARX neural network

Aydemir, Mustafa; Okumuş, Halil İbrahim

doi:10.1007/s00202-022-01726-x

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Finally, the results of preloading ultrasonic motors were analyzed, and the optimal preloading interval was determined through experimental results. Aydemir and Okumus [5] studied nonlinear problems, trained experimental data using NARX neural networks, and then determined the results through experiments. Jaiswal et al [6] used the NARX neural network to study nonlinear problems in order to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Ultrasonic Motor Control Based on NARX Neural Network

Yang,

Li,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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Compared with traditional motors, ultrasonic motors have the advantages of small size and no noise and are widely used in modern technological fields. This article proposes a neural network-based ultrasonic motor control method and optimizes it to address the difficulty of controlling ultrasonic motors. Firstly, the principle and equivalent circuit of ultrasonic motors are studied, and a phase-shifted PWM method suitable for driving ultrasonic motors is proposed. Secondly, to meet the nonlinear characteristics of ultrasonic motors, a NARX neural network that can be used for nonlinear systems is adopted for optimization control. Finally, an experimental platform was built for the experiment, and the results showed that the proposed H-bridge driving circuit has a wide input voltage range. After using the NARX neural network, the output voltage of the H-bridge circuit is more stable, which can provide a stable driving voltage for the ultrasonic motor. It can provide a certain reference value for the application of ultrasonic motors in modern technology.

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Section: Introductionmentioning

confidence: 99%