摘要：AbstractThanks to the continuously increasing computational power of CPUs, nowadays Model Predictive Control (MPC), initially designed for multi-variable control of chemical plants, is adopted in a large number of different applications, covering a wide range of process dynamics ranging from slow to even fast time scales. As a consequence, a renewed interest in numerical optimization tools, especially for nonlinear systems, arose. This work aims at demonstrating the feasibility of a novel methodology, based on a Linear Fractional Transform (LFT) formulation of the system dynamics, that allows to efficiently solve nonlinear MPC problems. An application example, concerning the tracking control of an autonomous vehicle, shows the effectiveness of the proposal.
