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We present Barrier-based Simplex (Bb-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. Bb-Simplex is centered around the Simplex Control Architecture, which consists of a high-performance advanced controller which is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We consider a significant application of Bb-Simplex to a microgrid featuring an advanced controller in the form of a neural network trained using reinforcement learning. The microgrid is modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller. 

We present the Distributed Simplex Architecture (DSA), a new runtime assurance technique that provides safety guarantees for multi-agent systems (MASs). DSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. The traditional Simplex approach is limited to single-agent systems or a MAS with a centralized control scheme. DSA addresses this limitation by extending the scope of Simplex to include MASs under distributed control. In DSA, each agent runs a local instance of traditional Simplex such that the preservation of safety in the local instances implies safety for the entire MAS. Control Barrier Functions play a critical role. They are used to define DSA’s core components (the baseline controller and the decision module’s switching logic between advanced and baseline controllers) and to verify the safety of a DSA instance in a distributed manner. We provide a general proof of safety for DSA, and present experimental results for several case studies, including flocking with collision avoidance, safe navigation of ground rovers through way-points, and the safe operation of a microgrid.