Sampled-Data Stabilization With Control Lyapunov Functions via Quadratically Constrained Quadratic Programs

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 continuous time models and discrete sampled implementation resulting controllers, often leading poor performance on hardware platforms. We propose an approach close this by synthesizing sampled-data counterparts these CLF-based specified as quadratically constrained (QCQPs). Assuming feedback linearizability stable zero-dynamics system's model, we derive practical stability guarantees system. demonstrate improved proposed over in simulation.