发表机构
University of Illinois Chicago; Texas A&M University（伊利诺伊大学芝加哥分校; 德克萨斯A&M大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于自适应共形分位数回归的安全控制框架，构建校准且状态相关的预测区间，结合概率控制屏障函数与MPC，实现高概率安全保证并降低保守性。
AI 中文摘要
不确定性下的安全关键控制需要既具有统计有效性（以保证可认证的性能）又能与可执行的安全约束兼容的不确定性表示。然而，现有方法通常假设特定的不确定性分布以获得可证明的安全保证，或建立对称且与输入无关的预测区间以实现鲁棒安全，这可能导致控制综合中的安全约束不匹配或过于保守。本文提出了一种新颖的具有自适应不确定性量化的安全控制框架，该框架构建校准且状态相关的预测区间，以实现高概率安全保证，同时提升受约束的控制性能。该框架利用自适应共形预测（ACP），并将其扩展至共形分位数回归（CQR），以捕获无分布假设、非对称的不确定性区间，并具有可认证的概率覆盖，同时将所得不确定性集整合到概率控制屏障函数公式中，以在降低保守性的同时强制执行鲁棒安全。这产生了不确定性感知的安全控制约束，可纳入模型预测控制（MPC）框架，以高概率提供可证明的安全行为。仿真和理论结果证明了我们方法的有效性。
英文摘要
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.
Comments8 pages, accepted to CDC 2026
