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：精确跟踪四旋翼的任意轨迹面临诸多挑战，包括未知非线性动力学、轨迹不可行性以及执行器限制。为解决这些问题，我们提出深度自适应轨迹跟踪（DATT）方法——一种基于学习的方案，能够在真实环境中存在大幅干扰的情况下，精确跟踪任意（可能不可行的）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环境中训练。部署于真实硬件时，DATT通过L1自适应控制闭环增强扰动估计能力，无需任何微调。在非稳态风场中，DATT在可行平滑轨迹与不可行轨迹上均显著优于竞争性自适应非线性控制器与模型预测控制器，包括基线方法完全失效的挑战性场景。此外，DATT可高效在线运行，推理时间低于3.2毫秒，不足自适应非线性模型预测控制基线的四分之一。