Data-Driven LightGBM Controller for Robotic Manipulator

This paper introduces a data-driven approach employing the Light Gradient Boosting Machine (LightGBM) algorithm as a controller for robotic manipulators. By harnessing data-driven techniques and machine learning, the method captures the intricate dynamics, uncertainties, and nonlinearities inherent in robotic manipulators, including variations in inertia, Coriolis terms, and torque disturbances. This comprehensive approach leads to more precise and flexible control strategies. The LightGBM model is trained on representative datasets, enabling it to discern underlying patterns and correlations between control inputs and desired manipulator responses, even in the presence of uncertainties. The proposed controller is evaluated through extensive simulations and real-world experiments, revealing superior performance with evaluation metrics such as Root Mean Squared Error (RMSE) of 0.584, Mean Absolute Error (MAE) of 0.132, and R-squared of 0.999 (99.9%). Additionally, the controller demonstrates a settling time of 0.528 seconds and an overshoot of 4.132%. These show the exceptional performance of the data-driven LightGBM controller, demonstrating its high accuracy, adaptability, and robustness. This research advances the capabilities of robotic manipulators through the integration of data-driven methodologies and machine-learning techniques.