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.

翻译：面向安全关键控制的不确定自适应系统往往依赖于保守的最坏情况不确定性界，这限制了闭环性能。在线共形预测是一种强大的数据驱动方法，适用于被预测输出真值在线揭示时的量化不确定性场景。然而，对于无需测量状态导数即可适应动态的系统，标准在线共形预测无法充分量化模型不确定性。我们提出交错积分在线共形预测（SI-OCP）算法，该算法利用积分得分函数量化扰动与学习误差的集总效应。该方法提供了长期覆盖保证，当与安全关键控制器（包括鲁棒管模型预测控制）结合时，可确保长期安全性。最后，我们通过基于鲁棒管MPC的全连接深度神经网络（DNN）自适应四旋翼飞行器数值仿真验证了所提方法，凸显了该方法对复杂学习参数化与控制策略的适用性。