跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1151新增论文
+896其中 AI 相关
+86已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描:2026-11-06 19:23 · 全站最近入库:2026-11-06 13:12各信源进度 →

统计口径与数据新鲜度

统计 2026-11-05 19:29 — 2026-11-06 19:29(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:2026-11-06 19:23最近发现新增版本:2026-11-06 13:12库内论文最新更新时间:2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,055 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常,查看状态 →

找到 1 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-06 19:21(北京时间)
版本更新 · v5arXiv:2511.15238

A Quantitative Approach to Reach-Avoid Reachability Analysis for Discrete-Time Nonlinear Control Systems

Ihab Tabbara, Eliya Badr, Hussein Sibai

A reach-avoid specification requires a dynamical system to reach a target set of states while avoiding a failure set. Reachability analysis partitions the state space into parts starting from which the specification can be satisfied by following some control signal and those from which it cannot despite the best control effort. In this paper, we present a quantitative approach for reachability analysis to analyze the satisfaction of avoid and reach-avoid specifications for discrete-time nonlinear control systems. In such an approach, a value function assigns a value to each state that determines how robustly the system can satisfy the specification starting from it and following the optimal control signal. However, computing such a value function is difficult. Instead, our approach constructs discrete abstractions on which it performs value iteration to compute sound bounds on the desired value function. The resulting lower bound enables a sound under-approximation of the reach avoid set (RAS) and, in the avoid case, a sound over-approximation of the backward reachable set (BRS). When the control set is finite, the upper bound additionally enables a sound over-approximation of the RAS and, in the avoid case, a sound under-approximation of the BRS. The lower bound also yields quantitative guarantees on safety margins and safe target-reaching times. Our approach further refines the abstraction by splitting the grid cells that could not be classified as within or outside the BRS (or RAS) to improve the usefulness of the bounds. We evaluate our approach on five example systems with up to five-dimensional state spaces.

尚无 AI 解读,展示原文摘要

eess.SY · cs.FL · cs.SC
arXiv ↗PDF ↗