Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals

2026-09-19

AI Summary

本文提出一种基于自适应保形分位数预测区间的安全控制框架，以解决不确定性条件下的安全关键控制问题，提高控制性能并确保高概率安全性。

Abstract

Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compatible with enforceable safety constraints. However, existing methods often assume particular distributions of uncertainty for provable safety guarantees or establish symmetric and input-agnostic prediction intervals for robust safety, which can lead to misaligned or overly conservative safety constraints in control synthesis. In this paper, we introduce a novel safe control framework with adaptive uncertainty quantification that constructs calibrated and state-dependent prediction intervals to enable high-probability safety guarantees, while improving constrained control performance. The framework leverages adaptive conformal prediction (ACP) and extends it with conformal quantile regression (CQR) to capture distribution-free, asymmetric uncertainty intervals with certifiable probabilistic coverage, and integrates the resulting uncertainty sets into a probabilistic control barrier function formulation to enforce robust safety with reduced conservativeness. This yields uncertainty-aware safe control constraints that can be incorporated within a model predictive control(MPC) framework to provide provably safe behaviors with high probability. Simulation and theoretical results are provided to demonstrate the effectiveness of our approach.

Problem

• safety-critical control
• uncertainty representation
• statistical validity
• safety constraints
• conservativeness

Innovation

• Adaptive Conformal Prediction
• Conformal Quantile Regression
• Probabilistic Control Barrier Function
• Uncertainty Quantification
• Safety-Critical Control