Title :
Minimal models to capture the dynamics of a rotary unmanned aerial vehicle
Author :
Choi, R.L.W. ; Hann, Christopher E. ; XiaoQi Chen
Author_Institution :
Dept. of Mech. Eng., Univ. of Canterbury, Christchurch, New Zealand
Abstract :
This paper presents a method for characterising the primary dynamics of a rotary unmanned aerial vehicle. Based on first principles and basic aerodynamics, a mathematical model which explains the rigid body dynamics of a model-scale helicopter is developed. The resulting model is reduced to three simplified decoupled model of attitude dynamics. Empirical test data is collected from a field experiment with significant wind disturbances. An integral based parameter identification method is presented to identify the unknown intrinsic helicopter parameters. An extended Kalman filter system identification method and common nonlinear regression are used for comparison. The EKF was found to be highly dependent on the initial states, so is not suitable for this application which contains significant disturbance and modelling errors. The proposed integral based parameter identification method was shown to be fast and accurate and is well suited to this application.
Keywords :
Kalman filters; aerodynamics; autonomous aerial vehicles; helicopters; nonlinear filters; regression analysis; robot dynamics; aerodynamics; attitude dynamics; extended Kalman filter system identification method; integral based parameter identification method; intrinsic helicopter parameter; mathematical model; model-scale helicopter; modelling error; nonlinear regression; rigid body dynamics; rotary unmanned aerial vehicle dynamics; simplified decoupled model; Aerodynamics; Computational modeling; Equations; Helicopters; Mathematical model; Parameter estimation; Rotors;
Conference_Titel :
Mechatronics and Machine Vision in Practice (M2VIP), 2012 19th International Conference
Print_ISBN :
978-1-4673-1643-9