Lifting-based nonlinear model predictive control for high-performance mobile robot tracking

Abstract

Nonlinear model predictive control (NMPC) is widely used in robotics due to its ability to handle nonlinear dynamics and explicitly enforce state and input constraints by solving an optimal control problem online. However, standard NMPC implementations typically rely on direct time discretization and evaluate costs and constraints only at sampling instants. This paper investigates the real-world effectiveness of a previously introduced lifting-based NMPC formulation that explicitly accounts for intersample responses by minimizing a continuous-time tracking error criterion. The formulation is implemented for trajectory tracking of a differential-drive robot and evaluated against a conventional NMPC approach. Real-world experiments on lemniscate and right-triangular reference trajectories, under variations in target speed and sampling period, show that the lifting-based NMPC consistently achieves lower tracking RMSE and mitigates corner-cutting at vertices. Solve-time measurements further confirm that the approach can be executed online on embedded hardware while maintaining real-time feasibility.