Current cyber-physical systems (CPS) are expected to accomplish complex tasks. To achieve this goal, high performance, but unverified controllers (e.g. deep neural network, black-box controllers from third parties) are applied, which makes it very challenging to keep the overall CPS safe. By sandboxing these controllers, we are not only able to use them but also to enforce safety properties over the controlled physical systems at the same time. However, current available solutions for sandboxing controllers are just applicable to deterministic (a.k.a. non-stochastic) systems, possibly affected by bounded disturbances. In this paper, for the first time we propose a novel solution for sandboxing unverified complex controllers for CPS operating in noisy environments (a.k.a. stochastic CPS). Moreover, we also provide probabilistic guarantees on their safety. Here, the unverified control input is observed at each time instant and checked whether it violates the maximal tolerable probability of reaching the unsafe set. If this probability exceeds a given threshold, the unverified control input will be rejected, and the advisory input provided by the optimal safety controller will be used to maintain the probabilistic safety guarantee. The proposed approach is illustrated empirically and the results indicate that the expected safety probability is guaranteed.

中文翻译：

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随机网络物理系统的沙盒控制器

当前的网络物理系统 (CPS) 有望完成复杂的任务。为了实现这一目标，应用了高性能但未经验证的控制器（例如深度神经网络、来自第三方的黑盒控制器），这使得保持整体 CPS 安全非常具有挑战性。通过对这些控制器进行沙箱处理，我们不仅能够使用它们，还能够同时在受控物理系统上强制执行安全属性。然而，当前可用的沙盒控制器解决方案仅适用于确定性（又名非随机）系统，可能会受到有界干扰的影响。在本文中，我们首次提出了一种新颖的解决方案，用于对在嘈杂环境中运行的 CPS（又名随机 CPS）进行沙箱化未经验证的复杂控制器。而且，我们还为他们的安全提供概率保证。在这里，在每个时刻观察未经验证的控制输入，并检查它是否违反了达到不安全集的最大可容忍概率。如果这个概率超过给定的阈值，未经验证的控制输入将被拒绝，最优安全控制器提供的建议输入将用于维持概率安全保证。所提出的方法是根据经验说明的，结果表明预期的安全概率是有保证的。最优安全控制器提供的咨询输入将用于维持概率安全保证。所提出的方法是根据经验说明的，结果表明预期的安全概率是有保证的。最优安全控制器提供的咨询输入将用于维持概率安全保证。所提出的方法是根据经验说明的，结果表明预期的安全概率是有保证的。