Enhanced sampled-data model predictive control via nonlinear lifting

TL;DR

This paper presents a new nonlinear model predictive control framework that uses a lifting technique to improve control accuracy and response time for nonlinear systems, validated through simulations.

Contribution

It introduces a novel nonlinear lifting technique within NMPC, combining FSFH approximations and numerical methods for better control of nonlinear systems.

Findings

Lifted NMPC reduces settling time compared to conventional NMPC.

Simulation results show improved control accuracy.

The approach is practical for real-time nonlinear control applications.

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.