Risk-averse Control and Dynamic Optimization: Bridging the Gap Between Robust and Stochastic Control KU Leuven
In this research project a novel framework for risk averse model predictive control (MPC) will be developed, as existing techniques like robust MPC are too conservative or in the case of stochastic MPC require knowledge on the uncertainty distribution. Model predictive control is an online optimization-based control scheme which was originally developed for chemical processes but nowadays has found applications in a wide range of fields, such ...