发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究恢复控制中安全模型区分的极限，提出可达-避免框架，推导暴露下界与安全恢复构造，并证明容限不足时公共输入必致失败，容限充足时按比例分配可安全恢复，且量化了反馈下的安全恢复概率。
AI 中文摘要
恢复控制旨在通过有限时间内结束的干预实现持久恢复。我们将这一目标表述为一个可达-避免问题，其中输入必须在撤出前将系统安全地带入期望的吸引域。两个候选模型共享双稳态基线动力学，但通过不同的驱动暴露分配累积不可逆损伤。我们推导了盆地转移所需暴露的下界，以及安全恢复的充分构造。当下界超过总容限时，任何能恢复任一模型的公共开环输入都会在至少一个模型下导致失败。在容限充足的情况下，按比例分配可在不进行辨识的情况下恢复两者。对于带有高斯噪声的输出样本反馈，我们根据备选模型下的失败风险界定了安全恢复概率。一个数值示例展示了两种感知机制下的证书。
英文摘要
Restorative control seeks lasting recovery through an intervention that ends in finite time. We formulate this objective as a reach-avoid problem in which the input must bring the system safely into a desired basin of attraction before withdrawal. Two candidates share bistable baseline dynamics but accumulate irreversible damage through different allocations of drive-generated exposure. We derive a lower bound on the exposure required for basin transfer and a sufficient construction for safe restoration. When the lower bound exceeds the summed tolerances, every common open-loop input that restores either model causes failure under at least one. With sufficient tolerance, proportional allocation restores both without identification. For feedback from output samples with Gaussian noise, we bound safe-restoration probability in terms of failure risk under the alternative model. A numerical example illustrates the certificates under two sensing regimes.
CommentsSubmitted to the 2027 American Control Conference (ACC 2027)
