  Controller design for nonlinear systems with Control Lyapunov Function (CLF)
based quadratic programs has recently been successfully applied to a diverse
set of difficult control tasks. These existing formulations do not address the
gap between design with continuous time models and the discrete time sampled
implementation of the resulting controllers, often leading to poor performance
on hardware platforms. We propose an approach to close this gap by synthesizing
sampled-data counterparts to these CLF-based controllers, specified as
quadratically constrained quadratic programs (QCQPs). Assuming feedback
linearizability and stable zero-dynamics of a system's continuous time model,
we derive practical stability guarantees for the resulting sampled-data system.
We demonstrate improved performance of the proposed approach over continuous
time counterparts in simulation.
