Cyber Physical Systems (CPS) have increasingly started using Learning Enabled Components (LECs) for performing perception-based control tasks. The simple design approach, and their capability to continuously learn has led to their widespread use in different autonomous applications. Despite their simplicity and impressive capabilities, these components are difficult to assure, which makes their use challenging. The problem of assuring CPS with untrusted controllers has been achieved using the Simplex Architecture. This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers. However, the key challenges in using the Simplex Architecture are: (1) designing an effective decision logic, and (2) sudden transitions between controller decisions lead to inconsistent system performance. To address these research challenges, we make three key contributions: (1) dynamic-weighted simplex strategy – we introduce “weighted simplex strategy” as the weighted ensemble extension of the classical Simplex Architecture. We then provide a reinforcement learning based mechanism to find dynamic ensemble weights, (2) middleware framework – we design a framework that allows the use of the dynamic-weighted simplex strategy, and provides a resource manager to monitor the computational resources, and (3) hardware testbed – we design a remote-controlled car testbed called DeepNNCar to test and demonstrate the aforementioned key concepts. Using the hardware, we show that the dynamic-weighted simplex strategy has 60% fewer out-of-track occurrences (soft constraint violations), while demonstrating higher optimized speed (performance) of 0.4 m/s during indoor driving than the original LEC driven system.

网络物理系统（CPS）越来越多地使用启用学习的组件（LEC）来执行基于感知的控制任务。简单的设计方法及其不断学习的能力导致它们在不同的自治应用程序中得到广泛使用。尽管它们具有简单性和令人印象深刻的功能，但是这些组件很难保证，这使它们的使用具有挑战性。使用Simplex体系结构已经实现了使用不受信任的控制器来确保CPS的问题。该体系结构将要确保的系统与安全的控制器集成在一起，并提供决策逻辑以在这些控制器的决策之间进行切换。但是，使用Simplex体系结构的主要挑战是：（1）设计有效的决策逻辑，（2）控制器决策之间的突然过渡会导致系统性能不一致。为了应对这些研究挑战，我们做出了三个关键贡献：（1）动态加权单纯形策略–我们引入“加权单纯形策略”作为经典单纯形体系结构的加权集合扩展。然后，我们提供一种基于强化学习的机制来查找动态集合权重；（2）中间件框架–我们设计了一个框架，该框架允许使用动态加权的单纯形策略，并提供资源管理器来监视计算资源，并且（3 ）硬件测试台–我们设计了一个称为DeepNNCar的遥控汽车测试台，以测试和演示上述关键概念。使用硬件，我们证明了动态加权的单纯形策略具有60％的超轨发生率（违反软约束），并且在室内驾驶过程中显示出比原始LEC更高的优化速度（性能），为0.4 m / s。系统。