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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.
We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Gene
Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramount importance to guarantee a successful deployment. Optimal centralize
We address the issue of safe optimal path planning under parametric uncertainties using a novel regularizer that allows trading off optimality with safety. The proposed regularizer leverages the notion that collisions may be modeled as constraint vio
High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber-physical systems (CPSs) to accomplish complex control missions. However, guaranteeing the safety and reliabil
For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning framework. Based on the simplified system model, a low-dimensional rep