Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic...

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Autores principales:	Muhammad Wasim, Ahsan Ali, Mohammad Ahmad Choudhry, Faisal Saleem, Inam Ul Hasan Shaikh, Jamshed Iqbal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/84c0d6084d4343b890518a5c516a2f19
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id	oai:doaj.org-article:84c0d6084d4343b890518a5c516a2f19
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spelling	oai:doaj.org-article:84c0d6084d4343b890518a5c516a2f192021-12-02T20:06:05ZUnscented Kalman filter for airship model uncertainties and wind disturbance estimation.1932-620310.1371/journal.pone.0257849https://doaj.org/article/84c0d6084d4343b890518a5c516a2f192021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257849https://doaj.org/toc/1932-6203An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.Muhammad WasimAhsan AliMohammad Ahmad ChoudhryFaisal SaleemInam Ul Hasan ShaikhJamshed IqbalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0257849 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Wasim Ahsan Ali Mohammad Ahmad Choudhry Faisal Saleem Inam Ul Hasan Shaikh Jamshed Iqbal Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
description	An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.
format	article
author	Muhammad Wasim Ahsan Ali Mohammad Ahmad Choudhry Faisal Saleem Inam Ul Hasan Shaikh Jamshed Iqbal
author_facet	Muhammad Wasim Ahsan Ali Mohammad Ahmad Choudhry Faisal Saleem Inam Ul Hasan Shaikh Jamshed Iqbal
author_sort	Muhammad Wasim
title	Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
title_short	Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
title_full	Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
title_fullStr	Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
title_full_unstemmed	Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.
title_sort	unscented kalman filter for airship model uncertainties and wind disturbance estimation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/84c0d6084d4343b890518a5c516a2f19
work_keys_str_mv	AT muhammadwasim unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation AT ahsanali unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation AT mohammadahmadchoudhry unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation AT faisalsaleem unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation AT inamulhasanshaikh unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation AT jamshediqbal unscentedkalmanfilterforairshipmodeluncertaintiesandwinddisturbanceestimation
_version_	1718375436785811456

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.

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