  This paper presents a control framework for magnetically actuated
micron-scale robots ($\mu$bots) designed to mitigate disturbances and improve
trajectory tracking. To address the challenges posed by unmodeled dynamics and
environmental variability, we combine data-driven modeling with model-based
control to accurately track desired trajectories using a relatively small
amount of data. The system is represented with a simple linear model, and
Gaussian Processes (GP) are employed to capture and estimate disturbances. This
disturbance-enhanced model is then integrated into a Model Predictive
Controller (MPC). Our approach demonstrates promising performance in both
simulation and experimental setups, showcasing its potential for precise and
reliable microrobot control in complex environments.
