.png){kind=icon}
.png){kind=icon}
Return
Enhanced Sampled-Data Model Predictive Control via Nonlinear Lifting
.png){kind=icon}
OA
AI
DOI：10.1002/rnc.70083.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 used in linear systems to capture intersample behavior, their application to nonlinear systems remains unexplored. We address this gap by formulating an NMPC scheme that combines fast-sample/fast-hold 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. The 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.
Keywords:
nonlinear lifting
nonlinear model predictive control
sampled-data systems
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.2
Papers:
6.9K
Citations:
1.4W