用在线学习动态调整安全控制器，让无人机在强风中避障仍安全。
Online Learning-Enhanced High Order Adaptive Safety Control
- 用神经微分方程在线更新高阶安全约束，实时适应模型变化。
- 在18km/h风速下控制38克微型无人机避障，保持安全距离。
- 适合需要实时安全保障的飞行器、机器人等复杂动态系统。
控制屏障函数（CBFs）是一种基于模型的有效工具，可形式化保证系统安全性。随着现代控制问题复杂度提升，CBFs因其可证明的安全性保障，在优化与学习驱动的控制领域备受关注，常作为安全过滤器使用。然而，其在真实系统中的成功应用高度依赖模型精度。例如载荷变化或风扰会显著影响飞行器动力学，破坏安全保证。本文提出一种高效且灵活的在线学习增强型高阶自适应控制屏障函数，采用神经微分方程实现。该方法能在复杂时变模型扰动下，实时提升CBF控制器的安全性。我们已在38克微型四旋翼上部署此混合自适应CBF控制器，在18km/h风速下成功保持与障碍物的安全距离。
原文摘要 · Abstract (English)
Control barrier functions (CBFs) are an effective model-based tool to formally certify the safety of a system. With the growing complexity of modern control problems, CBFs have received increasing attention in both optimization-based and learning-based control communities as a safety filter, owing to their provable guarantees. However, success in transferring these guarantees to real-world systems is critically tied to model accuracy. For example, payloads or wind disturbances can significantly influence the dynamics of an aerial vehicle and invalidate the safety guarantee. In this work, we propose an efficient yet flexible online learning-enhanced high-order adaptive control barrier function using Neural ODEs. Our approach improves the safety of a CBF controller on the fly, even under complex time-varying model perturbations. In particular, we deploy our hybrid adaptive CBF controller on a 38g nano quadrotor, keeping a safe distance from the obstacle, against 18km/h wind.
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