Open AccessUnscented Kalman filter for airship model uncertainties and wind disturbance estimation
Author(s) -
Muhammad Wasim,
Ahsan Ali,
M.A. Choudhry,
Faisal Saleem,
Inam Ul Hasan Shaikh,
Jamshed Iqbal
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0257849
Subject(s) - extended kalman filter , estimator , kalman filter , control theory (sociology) , aerodynamics , computer science , state vector , computation , controller (irrigation) , errors in variables models , control engineering , engineering , aerospace engineering , algorithm , mathematics , artificial intelligence , control (management) , physics , statistics , classical mechanics , machine learning , agronomy , biology
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.