Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

Abstract

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.

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

“…UKF accurately captures the mean and covariance up to the second order of Taylor series expansion for any nonlinear system [17]. The core of UKF is to recursively update the state and covariance of a nonlinear model through a nonlinear transformation-unscented transformation (UT).…”

Evaluation of Model-based Real-time Latency Compensation for Unmanned Aircraft Remote Control

“…This estimation is subject to a delay which varies with the measurement update rate. In this work, the error state estimation method is applied for estimating wind disturbances as in [13]. The estimated wind could potentially be used also for surveillance purposes in future.…”

Robust Design of Sliding Mode Control for Airship Trajectory Tracking with Uncertainty and Disturbance Estimation

“…To handle the model uncertainties and external disturbances, the SMC method is applied with modifications for airship trajectory tracking. In these modified methods, usually neural network (NN), fuzzy logic (FL), and adaptive methods are utilized for the approximation of model uncertainties and external disturbances [18][19][20][21][22][23][24][25][26].…”