LAUSR

—Ensuring robust constraint satisfaction for an infinite time horizon is a challenging, yet crucial task when deploying safety-critical systems. We address this issue by synthesizing robust control invariant sets for perturbed nonlinear sampled-data systems. This task can be encoded as a nonconvex program for which we propose a tailored, computationally efficient successive convexification algo- rithm. Based on the zonotopic representation of invariant sets, we obtain an updated candidate for the invariant set and the safety-preserving controller by solving a single convex program. To obtain a possibly large region of safe operation, our algorithm is designed so that the sequence of candidate invariant sets has monotonically increasing volume. We demonstrate the efficacy and scalability of our approach by applying it to a broad range of nonlinear con- trol systems from the literature with up to 20 dimensions.