Enhanced sampled-data model predictive control via nonlinear lifting

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. Supplementary Material File (ijrnc2024.pdf) - Download - 892.87 KB Information & Authors Information Version history Peer review timeline Published International Journal of Robust and Nonlinear Control Version of Record14 Jul 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 374views 210downloads Citations Download citation Nuthasith Gerdpratoom, Fumiya Matsuzaki, Yutaka Yamamoto, et al. Enhanced sampled-data model predictive control via nonlinear lifting. Authorea. 10 January 2025. DOI: https://doi.org/10.22541/au.173650879.91241440/v1 DOI: https://doi.org/10.22541/au.173650879.91241440/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.