Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT builds on a novel feedforward-feedback-adaptive control structure trained in simulation using reinforcement learning. When deployed on real hardware, DATT is augmented with a disturbance estimator using L1 adaptive control in closed-loop, without any fine-tuning. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields, including challenging scenarios where baselines completely fail. Moreover, DATT can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline

翻译：四旋翼的精确任意轨迹跟踪因未知非线性动力学、轨迹不可行性以及执行器限制而具有挑战性。为应对这些挑战，我们提出深度自适应轨迹跟踪（DATT），一种基于学习的方法，能够在现实世界存在大扰动的情况下精确跟踪任意、潜在不可行的轨迹。DATT基于一种新颖的前馈-反馈-自适应控制结构，该结构通过强化学习在仿真中训练。在实际硬件部署时，DATT借助L1自适应控制以闭环方式增强扰动估计器，无需任何微调。在不稳定风场中，对于可行光滑轨迹和不可行轨迹（包括基线算法完全失效的挑战性场景），DATT均显著优于竞争性自适应非线性控制器和模型预测控制器。此外，DATT能以低于3.2毫秒的推理时间高效在线运行，不足自适应非线性模型预测控制基线推理时间的四分之一。