用积分得分法量化扰动与学习误差，实现自适应系统的长期安全控制。
Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees
- 通过积分得分函数捕捉扰动和学习误差的综合影响
- 在多步预测中保持长期覆盖率，确保系统安全
- 适用于复杂神经网络控制的无人机系统，适合安全关键场景
安全关键型自适应系统在不确定环境下常依赖保守的最坏情况不确定性边界，限制了闭环性能。在线校准预测是一种强大的数据驱动方法，可在输出真实值在线反馈时量化不确定性；然而，对于不测量状态导数而动态自适应的系统，标准在线校准预测无法充分量化模型不确定性。本文提出分步积分在线校准预测（SI-OCP），利用积分得分函数量化扰动与学习误差的累积效应，提供长期覆盖率保证，在与安全关键控制器（如鲁棒管模型预测控制）结合时可实现长期安全性。最后，通过全层深度神经网络（DNN）自适应四旋翼机的数值仿真验证了该方法在复杂学习参数化与控制策略中的适用性。
原文摘要 · Abstract (English)
Safety-critical control of uncertain, adaptive systems often relies on conservative, worst-case uncertainty bounds that limit closed-loop performance. Online conformal prediction is a powerful data-driven method for quantifying uncertainty when truth values of predicted outputs are revealed online; however, for systems that adapt the dynamics without measurements of the state derivatives, standard online conformal prediction is insufficient to quantify the model uncertainty. We propose Staggered Integral Online Conformal Prediction (SI-OCP), an algorithm utilizing an integral score function to quantify the lumped effect of disturbance and learning error. This approach provides long-run coverage guarantees, resulting in long-run safety when synthesized with safety-critical controllers, including robust tube model predictive control. Finally, we validate the proposed approach through a numerical simulation of an all-layer deep neural network (DNN) adaptive quadcopter using robust tube MPC, highlighting the applicability of our method to complex learning parameterizations and control strategies.
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