Data-Driven LightGBM Controller for Robotic Manipulator

This dataset contains simulation data of the LightGBM controller for robotic manipulator. The data were generated using a closed-loop system of spacecraft attitude dynamics under an exact feedback linearization-based controller. The LightGBM controller was designed using supervised machine learning methodologies, and the training and testing datasets were generated from the input-output data of the closed-loop system. This dataset contains the results of simulations conducted to train a LightGBM controller for robotic manipulator using different data sizes ranging from 2,525 to 1,080,900 data points. The simulations were performed to demonstrate the importance of data size in training the LightGBM controller. The dataset can be used to analyze the performance of the LightGBM controller under various data sizes and to explore the relationship between data size and controller performance.