Enhancement of the Ball Balancing on the Plate using hybrid PD/Machine learning techniques

Abstract

Abstract This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.