发表机构
GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对紧密编队飞行四旋翼受空气动力学相互作用影响问题，提出基于物理的残差动力学学习框架，设计高效反馈线性化控制器，经硬件实验验证其能降低跟踪误差，在低计算量下达到NMPC跟踪性能，且短训练数据和低循环速率可实现稳定编队飞行。
AI 中文摘要
紧密编队飞行的四旋翼受到湍流空气动力学相互作用（如下洗）的严重影响，若未建模可能导致灾难性碰撞。为此，我们提出一个基于物理的残差动力学学习框架，它能捕捉复杂空气动力学相互作用，同时确保多四旋翼系统保持微分平坦性。利用这种保平坦性设计了计算高效的反馈线性化控制器，可通过前馈补偿消除空气动力学干扰。硬件实验表明，与标称基线相比，我们的框架将平均跟踪误差降低了31%。至关重要的是，我们的轻量级方法在计算量少一个数量级的情况下，与最先进的非线性模型预测控制（NMPC）具有相同的跟踪性能。我们首次证明，利用不到30秒的训练数据和5毫秒的循环速率就能实现稳定、紧密的编队飞行，为计算受限的飞行堆栈解锁高保真空气动力学补偿。
英文摘要
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
CommentsAccepted at IROS 26'
