通过保持系统微分平坦性，实现高精度、低延迟的四旋翼编队飞行控制。
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
- 基于物理信息的残差动力学学习，保留系统微分平坦性。
- 相比基线平均跟踪误差降低31%，且计算量仅为先进NMPC的十分之一。
- 仅需30秒训练数据与5毫秒控制周期，适合算力受限场景。
在紧密编队飞行中，四旋翼无人机易受湍流气动干扰（如下洗气流）影响，若不建模将导致灾难性碰撞。本文提出一种融合物理先验的残差动力学学习框架，既能捕捉复杂气动相互作用，又确保多机系统保持微分平坦性。利用该性质设计了计算高效的反馈线性化控制器，可通过线性控制方法简便调参，并通过前馈补偿消除气动干扰。硬件实验表明，本方法相比基准方案平均跟踪误差降低31%。关键的是，轻量化方案在计算量仅为最先进非线性模型预测控制（NMPC）的十分之一时，仍达到相当的跟踪性能。首次证明仅需30秒以下训练数据和5毫秒控制周期即可实现稳定紧密编队飞行，为计算资源受限的飞行系统解锁高保真气动补偿能力。实验视频见：https://www.youtube.com/watch?v=uF26IkRFQMk
原文摘要 · Abstract (English)
Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To compensate for these effects, we propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat. We leverage this preserved flatness to design a computationally efficient feedback linearization controller that is easily tunable with linear control techniques and cancels aerodynamic disturbances via feedforward compensation. Hardware experiments demonstrate our framework reduces average tracking errors by 31% compared to nominal baselines. Crucially, our lightweight approach matches the tracking performance of state-of-the-art nonlinear model predictive control (NMPC) while requiring an order of magnitude less computation. We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks. The video of our physical experiments can be found at https://www.youtube.com/watch?v=uF26IkRFQMk
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