让四旋翼自动调整控制器参数，实时提升轨迹跟踪精度。
TACO: Trajectory-Aware Controller Optimization for Quadrotors
- 根据未来轨迹和当前状态在线优化控制器增益。
- 相比固定参数，轨迹跟踪误差显著降低，且速度比黑箱优化快数个数量级。
- 适合需要高精度实时控制的无人机应用，如复杂飞行任务。
四旋翼飞行器在轨迹跟踪中的控制性能高度依赖参数调优，但传统方法通常采用固定的手动调参，牺牲了任务特异性表现。本文提出轨迹感知控制器优化框架TACO，可根据未来参考轨迹和当前飞行状态在线调整控制器参数。TACO结合学习型预测模型与轻量级优化算法，实现实时优化，适用于多种轨迹类型，并可进一步通过调整轨迹来提升动态可行性，同时满足平滑性约束。为支持大规模训练，我们开发了一个并行化的四旋翼仿真器，可在多样化轨迹上快速收集数据。实验表明，TACO在多种轨迹上均优于传统静态调参方法，且运行速度比黑箱优化基线快数个数量级，已在真实四旋翼平台上实现实用化实时部署。此外，利用TACO优化轨迹后，飞行器的跟踪误差显著降低。
原文摘要 · Abstract (English)
Controller performance in quadrotor trajectory tracking depends heavily on parameter tuning, yet standard approaches often rely on fixed, manually tuned parameters that sacrifice task-specific performance. We present Trajectory-Aware Controller Optimization (TACO), a framework that adapts controller parameters online based on the upcoming reference trajectory and current quadrotor state. TACO employs a learned predictive model and a lightweight optimization scheme to optimize controller gains in real time with respect to a broad class of trajectories, and can also be used to adapt trajectories to improve dynamic feasibility while respecting smoothness constraints. To enable large-scale training, we also introduce a parallelized quadrotor simulator supporting fast data collection on diverse trajectories. Experiments on a variety of trajectory types show that TACO outperforms conventional, static parameter tuning while operating orders of magnitude faster than black-box optimization baselines, enabling practical real-time deployment on a physical quadrotor. Furthermore, we show that adapting trajectories using TACO significantly reduces the tracking error obtained by the quadrotor.
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