An EKF Based Method for Path Following in Turbulent Air
(*) Corresponding author
An innovative use of the Extended Kalman Filter (EKF) is proposed to perform both accurate path following and adequate disturbance rejection in turbulent air. The tuned up procedure employs simultaneously two different EKF: the first one estimates gust disturbances, the second one estimates modified aircraft parameters. The first filter, by using measurements gathered in turbulent air, estimates both aircraft states and wind components. The second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances. The predictor of the second EKF uses the estimated wind components to solve motion equations in turbulent air. Besides a set of unknown stability and control parameters (containing displacements of the controls) is introduced into the predictor. This set contains modified aerodynamic coefficients. These ones are obtained by adding entirely new derivatives or synthetic increments to basic ones. Therefore, the above aforementioned unknown values of aircraft parameters augment the aircraft’s state. The filter estimates the augmented state by using a set of measurements formed by the desired flight path variables. In this way, it is possible to obtain the command laws by using the postulated unknown stability and control derivatives, which contain the control displacements. Therefore, the obtained control laws, related to either the characteristics of the disturbance or the desired flight path, are adaptive.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
Mitsutake, K., Higashino, S., Evaluation of an A*-EC Hybrid Path Planning Method for UAVs Using Real-Time Hardware-in-the-Loop Simulation, (2013) International Review of Aerospace Engineering (IREASE), 6 (1), pp. 40-47.
Sigurd K., How J., UAV Trajectory Design using Total field Collision Avoidance, AIAA Guidance, navigation and Control Conference and Exhibit (2013), Austin, TX, USA, 2013.
Wilburn, J., Perhinschi, M., Wilburn, B., Enhanced Modified Voronoi Algorithm for UAV Path Planning and Obstacle Avoidance, (2013) International Review of Aerospace Engineering (IREASE), 6 (1), pp. 54-63.
Bousson, K., Gameiro, T., A Quintic Spline Approach to 4D Trajectory Generation for Unmanned Aerial Vehicles, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 1-9.
Benzerrouk, H., Salhi, H., Nebylov, A., Non-Gaussian Sensor Fusion Analysis with “Gaussian Mixture and Adaptive” Based Cubature Kalman Filtering for Unmanned Aerial Vehicle, (2013) International Review of Aerospace Engineering (IREASE), 6 (6), pp. 264-277.
Mulgund S. S., Stengel R. F., Optimal Non-linear Estimation for Aircraft Flight Control in Wind Shear, (1996), Automatica, Vol. 32 pp.3-13.
Williams P., Landorp B., Ockels W., Flexible tethered kite with movable attachment points, part II: state and wind estimation, (2007), AIAA meeting papers, Vol 12.
Alonge F., Cangemi T., D'Ippolito F., Grillo C., Vitrano F. P., Estimation of turbulence and state based on EKF for tandem canard UAV, (2008) Automatic Control in Aerospace, Vol. 1 (2008), paper 3.
Welch G., Bishop G., An Introduction to the Kalman Filter (TR 95041, Department of Computer Science, University of North Carolina at Chapel Hill, 2004).
Etkin B., Dynamics of Atmospheric Flight (John Whiley and Sons, New York, 1972).
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2023 Praise Worthy Prize