  Safe control with guarantees generally requires the system model to be known.
It is far more challenging to handle systems with uncertain parameters. In this
paper, we propose a generic algorithm that can synthesize and verify safe
controllers for systems with constant, unknown parameters. In particular, we
use robust-adaptive control barrier functions (raCBFs) to achieve safety. We
develop new theories and techniques using sum-of-squares that enable us to pose
synthesis and verification as a series of convex optimization problems. In our
experiments, we show that our algorithms are general and scalable, applying
them to three different polynomial systems of up to moderate size (7D). Our
raCBFs are currently the most effective way to guarantee safety for uncertain
systems, achieving 100% safety and up to 55% performance improvement over a
robust baseline.
