Computing Safe Sets of Linear Sampled-Data Systems

Leveraging autonomous systems in safety-critical applications requires formal robustness guarantees against uncertainties. We address this issue by computing safe terminal sets with corresponding safety-preserving controllers, which ensure robust constraint satisfaction for an infinite time horizon. To maximize the region of operation, we also construct as large possible initial that can be safely steered into set finite time. use scalable reachability analysis and convex optimization to efficiently compute sampled-data systems. These are composed a physical plant evolving continuous digital controller being implemented discrete further verify effectiveness our control approach using simple double-integrator system vehicle-platooning benchmark.