Machine Learning Evaluation of Servo Motor Performance for Efficient Control Strategies

Servo motors are widely used in a broad range of applications, from industrial automation systems to robotics. The examination of servo motor mechanisms has a vital function in assessing the efficiency of industrial systems. This study investigates the performance of various machine learning models in classifying servo motor mechanisms according to their rise times. For classification, models such as Logistic Regression, Decision Tree, and Gradient Boosting using Scikit-learn, XGBoost, and CatBoost libraries were utilized. The primary aim is to analyze the impact of different motor and screw types, as well as the Pgain and Vgain parameters, on the performance of servo mechanisms. For this purpose, Linear Regression, Decision Tree, and Gradient Boosting models using Scikit-learn, XGBoost, and CatBoost libraries were used for regression tasks. This approach enables a clearer interpretation of how individual parameters influence servo mechanism performance. In the secondary aim of classifying servo motor mechanisms based on their rise times, Gradient Boosting models from Scikit-learn and CatBoost achieved the highest accuracy of 97.62%, reaching superior precision and recall values. Post-classification, the robustness of the models was confirmed by analyzing the confusion matrix, which demonstrated that the classes were predicted correctly with minimal error. The outcomes indicate that advanced machine learning methods can significantly enhance both the precision and consistency of servo mechanism performance classification, which in turn facilitates the implementation of more effective control strategies within industrial applications.