Accurate trajectory tracking control for quadrotors is essential for safe navigation in cluttered environments. However, this is challenging in agile flights due to nonlinear dynamics, complex aerodynamic effects, and actuation constraints. In this article, we empirically compare two state-of-the-art control frameworks: the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC), by tracking a wide variety of agile trajectories at speeds up to 20 m/s (i.e.,72 km/h). The comparisons are performed in both simulation and real-world environments to systematically evaluate both methods from the aspect of tracking accuracy, robustness, and computational efficiency. We show the superiority of NMPC in tracking dynamically infeasible trajectories, at the cost of higher computation time and risk of numerical convergence issues. For both methods, we also quantitatively study the effect of adding an inner-loop controller using the incremental nonlinear dynamic inversion (INDI) method, and the effect of adding an aerodynamic drag model. Our real-world experiments, performed in one of the world's largest motion capture systems, demonstrate more than 78% tracking error reduction of both NMPC and DFBC, indicating the necessity of using an inner-loop controller and aerodynamic drag model for agile trajectory tracking.

翻译：四旋翼在杂乱环境中的安全导航离不开精确的轨迹跟踪控制。然而，由于非线性动力学、复杂气动效应及执行机构约束，敏捷飞行中的轨迹跟踪极具挑战性。本文通过跟踪速度高达20米/秒（即72公里/小时）的多种敏捷轨迹，对两种先进控制框架——非线性模型预测控制器（NMPC）与微分平坦性控制器（DFBC）——进行了实证对比。我们在仿真与真实环境中开展系统化比较，从跟踪精度、鲁棒性与计算效率三个维度评估两种方法。研究表明，NMPC在跟踪动态不可行轨迹方面具有优势，但代价是计算时间更长且存在数值收敛风险。针对两种方法，我们还定量研究了引入增量非线性动态反演（INDI）方法的内环控制器及气动阻力模型的效果。我们在全球最大运动捕捉系统中进行的真实世界实验表明，NMPC与DFBC的跟踪误差均降低超过78%，验证了内环控制器与气动阻力模型对敏捷轨迹跟踪的必要性。