An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics

TL;DR

This paper introduces a data-driven method to learn a stable approximate inverse for non-minimum phase systems, enhancing impromptu trajectory tracking without requiring explicit system models.

Contribution

It proposes a novel learning-based approach that ensures stability and high accuracy in trajectory tracking for non-minimum phase systems directly from input-output data.

Findings

The approach guarantees stability in non-minimum phase systems.

Including more training information can cause instability.

The method outperforms traditional inversion techniques in experiments.

Abstract

This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability. In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques. In this work, we propose an approach for learning a stable, approximate inverse of a non-minimum phase baseline system directly from its input-output data. Through theoretical discussions, simulations, and experiments on two different platforms, we show the stability of our proposed approach and its effectiveness for high-accuracy, impromptu tracking. Our approach also shows that including more information in the training, as is commonly assumed to be useful, does not lead to better performance but may trigger instability and impact the effectiveness of the overall approach.