Tracking error learning control for precise mobile robot path tracking in outdoor environment

TL;DR

This paper introduces a novel Tracking-Error Learning Control (TELC) algorithm that enhances mobile robot path tracking accuracy in outdoor environments by adaptively updating control actions and eliminating feedback control once on track.

Contribution

The TELC algorithm adaptively updates feedforward control to improve path tracking precision and guarantees convergence to the global minimum, outperforming traditional methods in outdoor terrain.

Findings

TELC achieves less than 10 cm tracking error in off-road terrain.

TELC outperforms traditional tracking error-based control methods.

The algorithm guarantees stability and convergence to the global minimum.

Abstract

This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than $10$ cm error in off-road terrain.