Computationally Tractable Robust Nonlinear Model Predictive Control using DC Programming
This framework creates a computationally efficient robust Model Predictive Control (MPC) system for nonlinear systems by using difference-of-convex (DC) programming. It uses data-driven methods to approximate complex nonlinear dynamics as DC functions, allowing the optimization problem to be solved quickly using convex techniques. This improves the balance between computational speed and guaranteeing stability in control.