Velocity and Disturbance Robust Non-linear Estimator for Autonomous Surface Vehicles with Reduced Sensing Capabilities

TL;DR

This paper introduces a robust non-linear state estimator for autonomous surface vehicles that accurately reconstructs velocities and disturbances using limited measurements, improving performance over existing methods.

Contribution

The paper proposes a novel, easily tunable non-linear estimator capable of reconstructing velocities and disturbances with reduced sensing requirements.

Findings

Outperforms previous estimators in simulations

Effectively reconstructs velocities and disturbances

Easily tunable parameters for noise and disturbance trade-off

Abstract

This paper presents a robust non-linear state estimator for autonomous surface vehicles, where the movement is restricted to the horizontal plane. It is assumed that only the vehicle position and orientation can be measured, being the former affected by bounded noises. Then, under some fair standard assumptions concerning the maximum velocities and acceleration rates of the vehicle, the estimator is able to reconstruct not only the velocities, but also the lumped generalised disturbances, that cluster external disturbances, non-linearities, and unmodelled dynamics. The observer is easily tunable by the user, with a set of four scalars, two of them related to the velocity of convergence of the estimator, and the other two parameters to set the desired trade-off between noise sensitivity and disturbance rejection. Several simulations with a well-known test-bed craft are provided to show how the proposed algorithm outperforms previous ones in the literature.