Learning-Based Geometric Tracking Control for Rigid Body Dynamics

This letter investigates learning-based geometric tracking control for rigid body dynamics without precise system model parameters. Our approach leverages recent advancements in geometric optimal control and data-driven techniques to develop a learning-based tracking solution. By adopting Lie algebra formulation to transform tracking dynamics into a vector space, we estimate unknown parameters from data, achieving robust and efficient learning. Compared to existing learning-based methods, our approach ensures geometric consistency and delivers superior tracking accuracy. The simulation results validate the effectiveness of our method.