Hybrid Model-Based and Data-Driven Wind Velocity Estimator for an Autonomous Robotic Airship
Abstract
In the context of autonomous airships, several works in control and guidance use wind velocity to design a control law. However, in general, this information is not directly measured in robotic airships. This paper presents three alternative versions for estimation of wind velocity. Firstly, an Extended Kalman Filter is designed as a model-based approach. Then a Neural Network is designed as a data-driven approach. Finally, a hybrid estimator is proposed by performing a fusion between the previous designed estimators: model-based and data-driven. All approaches consider only Global Positioning System (GPS), Inertial Measurement Unit (IMU) and a one dimensional Pitot tube as available sensors. Simulations in a realistic nonlinear model of the airship suggest that the cooperation between these two techniques increases the estimation performance.
Cite
@article{arxiv.1907.06266,
title = {Hybrid Model-Based and Data-Driven Wind Velocity Estimator for an Autonomous Robotic Airship},
author = {Apolo Silva Marton and André Ricardo Fioravanti and José Raul Azinheira and Ely Carneiro de Paiva},
journal= {arXiv preprint arXiv:1907.06266},
year = {2020}
}
Comments
This is a pre-print submitted for a Springer Journal (accepted for publication). It contains 12 pages (in two column format) and 15 figures