Machine Learning-Assisted Output Optimization of Non-Resonant Motors

The precision drive industry has seen rapid growth, leading to an increased demand for actuators that are both highly accurate and responsive. Among these, non-resonant piezoelectric motors are particularly noteworthy. These motors are extensively employed in applications such as high-precision manufacturing, precision drug delivery, and cellular puncture, owing to their adaptable drive control and resistance to external disturbances. Given the specific requirements of these applications, it is crucial to quickly determine the relationship between the motor input parameters and output characteristics—a challenging endeavor. In this research, we examine a typical non-resonant piezoelectric motor using multiple sets of experimental data. A machine learning algorithm is employed to swiftly establish the correlation between electromechanical input parameters and output trajectory characteristics. Data are analyzed using a random forest model to understand the underlying influence mechanisms. Based on this analysis, predictions and recommendations are made to achieve optimal operating conditions for the motor. This study demonstrates that machine learning serves as an effective tool for predicting piezoelectric motor performance, facilitating rapid assessment of motor output capabilities.