神经屏障：一种在线可认证的自适应高阶安全关键控制
Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control
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中文总结 AI 辅助
提出一种基于神经ODE和保形预测的在线可认证自适应控制屏障函数，在模型扰动下保证安全并量化不确定性，减少保守性。
中文摘要 AI 辅助
控制屏障函数是一种基于模型的有效工具，用于正式认证系统的安全性。然而，将其理论保证应用于现实世界的机器人系统需要高模型保真度。例如，载荷或风扰动可能导致飞行器出现显著的模型扰动，从而危及安全。在本工作中，我们提出了一种可认证的在线学习增强型鲁棒自适应控制屏障函数，该函数利用神经ODE适应扰动，并通过保形预测量化其自适应不确定性。我们的方法在未知时变模型扰动下保证所有时刻的安全性。当自适应不确定性较高时，它采用保守策略；随着接收更多数据，它高效适应以减少控制器的保守性。我们的方法在底层模型和轨迹满足适当的Lipschitz平滑性假设下，提供了具有概率界限的可证明安全性保证。这些结果展示了我们的方法作为在模型扰动下运行的机器人系统的实用安全控制器的潜力。
英文摘要
Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarantees to real-world robotics systems requires high model fidelity. For example, payloads or wind disturbances can cause significant model perturbations to an aerial vehicle, leading to safety compromises. In this work, we propose a certifiable online learning-enhanced robust adaptive control barrier function, which adapts to disturbances using a Neural ODE and quantifies its adaptation uncertainty with conformal prediction. Our approach guarantees safety at all time under unknown time-varying model disturbances. It adopts a conservative strategy when the adaptation uncertainty is high; and efficiently adapts to reduce controller conservativeness as it receives more data. Our approach provides a provable safety guarantee with a probability bound under suitable Lipschitz smoothness assumptions on the underlying model and trajectory. These results demonstrate the potential of our method as a practical safety controller for robotics system operating under model perturbations.
发表机构
- Brown University（布朗大学）
- University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
- California Institute of Technology（加州理工学院）
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