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.

References in corpus (7)

• A Tree-based Next-best-trajectory Method for 3D UAV Exploration
• A Nonlinear Model Predictive Control Strategy for Autonomous Racing of Scale Vehicles
• Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance in Unknown Obstructed Environments
• Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC
• Hypertracking and Hyperrejection: Control of Signals beyond the Nyquist Frequency
• Nonlinear Sampled-data Systems--A Lifting Framework
• Optimal Sampled-Data Control of a Nonlinear System