Optimization of Ultrasonic Motor Control Based on NARX Neural Network

Abstract

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.