有限数据下动态非对称驱动安全信息性

中文摘要 AI 辅助

当系统模型未被完全已知时，测量误差和有限的激励可能使多个模型与相同的有限数据保持一致。对一个模型判定为安全的指令可能对另一个模型失效，而有限的控制权限可能妨碍为保持安全所需的纠正动作。为确保在模型不确定性和非对称输入限制下的安全性，我们开发了一种有限数据证书，用于确定指令能否强制执行规定的安全不等式。对于具有精确已知回归量和有界聚合残差的线性参数化安全通道，我们推导出所有数据一致模型最坏情况下安全贡献的支持公式。该公式识别出允许有限界的回归方向，使得秩亏记录能够对安全认证做出贡献。利用执行器跟踪误差的认证分量界，得到一个仿射不等式，并给出逐点指令可行性的充要检验。该仿射不等式将最近认证指令的计算简化为标量求根问题。它还产生一个闭式门，用于选择规定指令段的最大认证分数。所提出的证书在其工作域内保证输出安全性，前提是反馈局部Lipschitz连续且不确定性界保持有效。域保持和全状态延续将此保证扩展到所有时间。一项车辆研究表明，在具有模型和执行器不确定性的安全关键环境中，可以从有限测量中认证输出安全性。

英文摘要

When the system model is not fully known, measurement error and limited excitation can leave several models consistent with the same finite data. A command judged safe for one model may fail for another, while limited control authority can prevent the corrective action needed to preserve safety. To ensure safety under model uncertainty and asymmetric input limits, we develop a finite-data certificate that determines whether a command can enforce a prescribed safety inequality. For a linearly parameterized safety channel with exactly known regressors and bounded aggregate residual error, we derive a support formula for the worst-case safety contribution of all data-consistent models. The formula identifies the regressor directions that admit a finite bound, allowing rank-deficient records to contribute to safety certification. Using certified componentwise bounds on actuator tracking error yields an affine inequality with a necessary and sufficient test for pointwise command feasibility. The affine inequality reduces computation of the closest certified command to a scalar root-finding problem. It also yields a closed-form gate that selects the largest certified fraction of a prescribed command segment. The proposed certificate guarantees output safety within its operating domain, provided the feedback is locally Lipschitz and the uncertainty bounds remain valid. Domain retention and full-state continuation extend this guarantee to all time. A vehicle study demonstrates that output safety can be certified from finite measurements in a safety-critical setting with model and actuator uncertainty.

发表机构

• University of Cincinnati（辛辛那提大学）
• The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）

机构由 AI 辅助整理，请以论文原文为准。