Enhanced sampled-data model predictive control via nonlinear lifting

Abstract: This paper introduces a novel nonlinear model predictive control (NMPC) framework that incorporates a lifting technique to enhance control performance for nonlinear systems. While the lifting technique has been widely employed in linear systems to capture intersample behaviour, their application to nonlinear systems remains unexplored. We address this gap by formulating an NMPC scheme that combines fast-sample fast-hold (FSFH) approximations and numerical methods to approximate system dynamics and cost functions. The proposed approach is validated through two case studies: the Van der Pol oscillator and the inverted pendulum on a cart. Simulation results demonstrate that the lifted NMPC outperforms conventional NMPC in terms of reduced settling time and improved control accuracy. These findings underscore the potential of the lifting-based NMPC for efficient control of nonlinear systems, offering a practical solution for real-time applications.