Department of Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan.
Department of Electrical Engineering, Riphah International University, Islamabad, Pakistan.
PLoS One. 2021 Nov 5;16(11):e0257849. doi: 10.1371/journal.pone.0257849. eCollection 2021.
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.
飞艇比空气动力飞行器轻，在通信、空中检查、边境监视和精准农业等领域具有巨大的应用潜力。飞艇模型由动力、空气动力、空气静力和推进力组成。然而，空气动力的计算仍然是一个挑战。除了空气动力模型的缺陷，飞艇质量矩阵还受到参数变化的影响。此外，由于飞艇的轻于空气的性质，它也容易受到风的干扰。这些建模问题是为飞艇开发高效自主飞行控制器的关键挑战。本文提出了一种使用 Unscented Kalman Filter (UKF) 对飞艇状态、模型不确定性和风干扰进行统一估计的方法。该方法基于集中式模型不确定性向量，将模型不确定性和风干扰统一在一个向量中。通过将六个辅助状态变量纳入集中式模型不确定性向量，对飞艇模型进行了扩展。使用自主开发的 UETT 飞艇的非线性仿真模型评估了所提出方法的性能，并通过进行误差分析进行了验证。为了进行比较研究，还开发了 EKF 估计器。结果表明，与 EKF 相比，所提出的估计器具有性能优势，但在所提出的估计器在计算方面略贵。然而，根据当前应用的要求，所提出的估计器可以是首选。
