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.

翻译：本文针对敏捷四旋翼飞行开发了计算高效的数据驱动模型预测控制方法。高速飞行中的敏捷四旋翼可能受到显著的气动效应影响。建模这些湍流气动效应是一项繁琐的任务，且所得模型可能过于复杂且计算上不可行。将高斯过程回归模型与系统的简单动力学模型相结合，已展现出显著的控制性能提升。然而，将GP模型直接集成到MPC流程中会给优化过程带来巨大的计算负担。因此，我们提出一种方法，通过在线MPC优化前利用参考轨迹和当前状态测量值计算模型修正项，将GP模型从MPC流程中分离。该方法已在Gazebo仿真环境中得到验证，轨迹跟踪误差降低高达50%，在匹配直接GP集成方法性能的同时提升了计算效率。