  Safety and stability are common requirements for robotic control systems;
however, designing safe, stable controllers remains difficult for nonlinear and
uncertain models. We develop a model-based learning approach to synthesize
robust feedback controllers with safety and stability guarantees. We take
inspiration from robust convex optimization and Lyapunov theory to define
robust control Lyapunov barrier functions that generalize despite model
uncertainty. We demonstrate our approach in simulation on problems including
car trajectory tracking, nonlinear control with obstacle avoidance, satellite
rendezvous with safety constraints, and flight control with a learned ground
effect model. Simulation results show that our approach yields controllers that
match or exceed the capabilities of robust MPC while reducing computational
costs by an order of magnitude.
