Model Predictive Path-Following for Constrained Differentially Flat Systems

TL;DR

This paper introduces a novel predictive path-following control method for differentially flat systems, enhancing robustness and performance in autonomous robots by transforming the control problem into a convex optimization and adapting to disturbances.

Contribution

It proposes a new coupling of feedforward linearization with path-based model predictive control leveraging differential flatness, enabling convex optimization and dynamic path reference adjustment.

Findings

Improved path adherence in quadrotor experiments

Enhanced robustness against disturbances like wind

Reduced control problem to convex optimization

Abstract

For many tasks, predictive path-following control can significantly improve the performance and robustness of autonomous robots over traditional trajectory tracking control. It does this by prioritizing closeness to the path over timed progress along the path and by looking ahead to account for changes in the path. We propose a novel predictive path-following approach that couples feedforward linearization with path-based model predictive control. Our approach has a few key advantages. By utilizing the differential flatness property, we reduce the path-based model predictive control problem from a nonlinear to a convex optimization problem. Robustness to disturbances is achieved by a dynamic path reference, which adjusts its speed based on the robot's progress. We also account for key system constraints. We demonstrate these advantages in experiment on a quadrotor. We show improved performance over a baseline trajectory tracking controller by keeping the quadrotor closer to the desired path under nominal conditions, with an initial offset and under a wind disturbance.