A New Practical Wind Estimation Technique For Unmanned Helicopters

Abstract

In this study, the authors propose a new model-based algorithm for atmospheric disturbances estimation using an inertial navigation system and Pitot measurements. The linear model of helicopter dynamics is modified into a quasi-nonlinear model by adding trim data and nonlinear kinematic equations. A nonlinear Kalman filter is designed based on this quasi-nonlinear model, utilizing a scaled Unscented Transformation. The resulting estimate was split using moving averages into sustained horizontal wind and atmospheric fluctuations. The paper presents validation results obtained by flight testing under hover conditions. Wind characteristics were measured with an ultrasonic sensor and the results were compared with the estimates. CIFER software was used to identify the refined gust matrix in the frequency domain. The authors introduced a simple wind dynamics model and incorporated a gust matrix to account for angular velocity components. The results indicate that the estimation of horizontal atmospheric disturbances can be improved with this approach.