Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees

TL;DR

The paper introduces SI-OCP, a novel online conformal prediction method with integral scoring for safe adaptive control, providing long-term safety guarantees in uncertain systems.

Contribution

It develops SI-OCP, an integral-based online conformal prediction algorithm that offers long-run coverage guarantees for adaptive, safety-critical control systems.

Findings

SI-OCP achieves long-term safety guarantees in simulations.

The method effectively quantifies model uncertainty without derivative measurements.

Validated on a neural network-based quadcopter control simulation.

Abstract

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.