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Safe CPS from Unsafe Controllers
arXiv - CS - Software Engineering Pub Date : 2021-02-24 , DOI: arxiv-2102.12981
Usama Mehmood, Stanley Bak, Scott A. Smolka, Scott D. Stoller

In this paper, we explore using runtime verification to design safe cyber-physical systems (CPS). We build upon the Simplex Architecture, where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain system safety. New to our approach, we remove the requirement that the baseline controller is statically verified. This is important as there are many types of powerful control techniques -- model-predictive control, rapidly-exploring random trees and neural network controllers -- that often work well in practice, but are difficult to statically prove correct, and therefore could not be used before as baseline controllers. We prove that, through more extensive runtime checks, such an approach can still guarantee safety. We call this approach the Black-Box Simplex Architecture, as both high-level controllers are treated as black boxes. We present case studies where model-predictive control provides safety for multi-robot coordination, and neural networks provably prevent collisions for groups of F-16 aircraft, despite occasionally outputting unsafe actions.

中文翻译:

来自不安全控制器的安全CPS

在本文中,我们探索使用运行时验证来设计安全的网络物理系统(CPS)。我们建立在Simplex体系结构的基础上,在该体系结构中,控制权限可能会从未经验证且可能不安全的高级控制器切换到备用基准控制器,以维护系统安全。对于我们的方法而言,新方法消除了对基线控制器进行静态验证的要求。这很重要,因为有许多类型的强大控制技术-模型预测控制,快速探索的随机树和神经网络控制器-在实践中通常效果很好,但是很难静态证明正确,因此无法以前用作基准控制器。我们证明,通过更广泛的运行时检查,这种方法仍然可以保证安全。我们将这种方法称为“黑匣子单纯形体系结构”,因为这两个高级控制器都被视为黑匣子。我们提供了案例研究,其中模型预测控制为多机器人协调提供了安全性,尽管偶尔会输出不安全的动作,但神经网络可证明可防止F-16飞机群发生碰撞。
更新日期:2021-02-26
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