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：摘要：由于未知非线性动力学、轨迹不可行性以及执行机构限制，实现四旋翼飞行器的精确任意轨迹跟踪极具挑战性。为应对这些挑战，我们提出了深度自适应轨迹跟踪（DATT）方法，这是一种基于学习的方法，能够在现实世界中存在较大干扰的情况下精确跟踪任意（甚至可能不可行）的轨迹。DATT构建了一种新颖的前馈-反馈-自适应控制结构，该结构通过强化学习在仿真环境中训练而成。在部署至真实硬件时，DATT在闭环中利用L1自适应控制引入扰动估计器，无需任何微调。在非稳定风场中，对于可行平滑轨迹和不可行轨迹，DATT均显著优于竞争性的自适应非线性控制器和模型预测控制器，甚至包括基线方法完全失效的挑战性场景。此外，DATT能够高效地在线运行，其推理时间低于3.2毫秒，不到自适应非线性模型预测控制基线的四分之一。