Data-driven learning of feedback policies for robust model predictive control: An approximation-theoretic view
Auditorium (AE 005), Department of Aerospace EngineeringModel Predictive Control (MPC) is a widely used optimization-based framework for the synthesis of feedback control, with mature theory and practice in the linear setting. Yet computational tractability remains a key bottleneck—particularly for robust nonlinear min-max MPC—because solving a (robust) optimization problem at every step is expensive and often intractable in practice. Explicit or approximate MPC […]