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The Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) modelpredictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands. We further show how to safely blend commands from the advanced and baseline controllers in multiagent systems, reducing the performance impact when switching is necessary to preserve safety. 

he Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) model-predictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands. 

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. 

We introduce the concept of Distributed Model Predictive Control (DMPC) with Acceleration-Weighted Neighborhooding (AWN) in order to synthesize a distributed and symmetric controller for high-speed flocking maneuvers (angular turns in general). Acceleration-Weighted Neighborhooding exploits the imbalance in agent accelerations during a turning maneuver to ensure that actively turning agents are prioritized. We show that with our approach, a flocking maneuver can be achieved without it being a global objective. Only a small subset of the agents, called initiators, need to be aware of the maneuver objective. Our AWN-DMPC controller ensures this local information is propagated throughout the flock in a scale-free manner with linear delays. Our experimental evaluation conclusively demonstrates the maneuvering capabilities of a distributed flocking controller based on AWN-DMPC.