An intelligent decision support tool for a travelling wave ultrasonic motor based on k-nearest neighbor algorithm

Abstract

Driving frequency, amplitude and phase difference of two-phase sinusoidal voltages are the input parameters which have influence on speed stability of travelling wave ultrasonic motors (TWUSMs).These parameters are also time-varying due to the variations in operating temperature. In addition, a complete mathematical model of the TWUSM has not been derived yet. Owing to these reasons, a machine learning approach is required for determining the compatibility of operating parameters related to speed stability of TWUSMs. For this purpose, an intelligent decision support tool has been designed for TWUSMs in this study. The input parameters such as driving frequency, amplitude, phase difference of two-phase sinusoidal voltages and operating temperature were evaluated by the k-nearest neighbor algorithm in the decision support tool. The results have shown that the proposed tool provides effective results in the compatibility determination of operating parameters related to speed stability of TWUSMs.