  This paper develops computationally efficient data-driven model predictive
control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed
flights can experience high levels of aerodynamic effects. Modeling these
turbulent aerodynamic effects is a cumbersome task and the resulting model may
be overly complex and computationally infeasible. Combining Gaussian Process
(GP) regression models with a simple dynamic model of the system has
demonstrated significant improvements in control performance. However, direct
integration of the GP models to the MPC pipeline poses a significant
computational burden to the optimization process. Therefore, we present an
approach to separate the GP models to the MPC pipeline by computing the model
corrections using reference trajectory and the current state measurements prior
to the online MPC optimization. This method has been validated in the Gazebo
simulation environment and has demonstrated of up to $50\%$ reduction in
trajectory tracking error, matching the performance of the direct GP
integration method with improved computational efficiency.
