用神经网络增强的飞行控制器，能稳定追踪极限飞行动作。
Learning Robust Agile Flight Control with Stability Guarantees
- 融合神经网络与非线性反馈，提升控制精度与效率。
- 在高扰动下仍保持稳定，可追踪超越执行器能力的激进轨迹。
- 仿真训练快且稳定，直接部署无需调参，适合真实飞行平台。
在高速敏捷四旋翼飞行的发展中，实现平台运行极限下的精确轨迹跟踪至关重要。控制器需应对执行器约束、具备对扰动的鲁棒性，并在安全关键应用中保持计算高效。本文提出一种新型神经增强反馈控制器，克服了现有先进控制范式的个体局限，整合其优势。实验表明，该控制器能够准确跟踪远超执行器可行范围的激进轨迹。尤为关键的是，控制器提供普适稳定性保障，在极端扰动环境下仍显著提升鲁棒性与跟踪性能。其非线性反馈结构计算高效，支持高频更新。此外，仿真中的学习过程快速且稳定，控制器固有的鲁棒性使其可直接部署至真实平台，无需训练增强或微调。
原文摘要 · Abstract (English)
In the evolving landscape of high-speed agile quadrotor flight, achieving precise trajectory tracking at the platform's operational limits is paramount. Controllers must handle actuator constraints, exhibit robustness to disturbances, and remain computationally efficient for safety-critical applications. In this work, we present a novel neural-augmented feedback controller for agile flight control. The controller addresses individual limitations of existing state-of-the-art control paradigms and unifies their strengths. We demonstrate the controller's capabilities, including the accurate tracking of highly aggressive trajectories that surpass the feasibility of the actuators. Notably, the controller provides universal stability guarantees, enhancing its robustness and tracking performance even in exceedingly disturbance-prone settings. Its nonlinear feedback structure is highly efficient enabling fast computation at high update rates. Moreover, the learning process in simulation is both fast and stable, and the controller's inherent robustness allows direct deployment to real-world platforms without the need for training augmentations or fine-tuning.
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