Title: Recursive Gauss-Newton based training algorithm for neural network modelling of an unmanned rotorcraft dynamics

Authors: Syariful S. Shamsudin; XiaoQi Chen

Addresses: Department of Aeronautical Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia ' Mechanical Engineering Department, University of Canterbury, Christchurch 8041, New Zealand

Abstract: The ability to model the time varying dynamics of an unmanned rotorcraft is an important aspect in the development of adaptive flight controller. This paper presents a recursive Gauss-Newton based training algorithm to model the attitude dynamics of a small scale rotorcraft based unmanned aerial system using the neural network (NN) modelling approach. It focuses on selection of optimised network for recursive algorithm that offers good generalisation performance with the aid of the cross validation method proposed. The recursive method is then compared with the off-line Levenberg-Marquardt (LM) training method to evaluate the generalisation performance and adaptability of the model. The results indicate that the recursive Gauss-Newton (rGN) method gives slightly lower generalisation performance compared with its off-line counterpart but adapts well to the dynamic changes that occur during flight. The proposed recursive algorithm was found effective in representing helicopter dynamics with acceptable accuracy within the available computational time constraint.

Keywords: artificial neural networks; ANNs; system identification; rotorcraft dynamics; unmanned aerial vehicles; UAVs; recursive Gauss-Newton; time varying dynamics; dynamic modelling; adaptive flight control; attitude dynamics; helicopter dynamics; helicopters.

DOI: 10.1504/IJISTA.2014.059300

International Journal of Intelligent Systems Technologies and Applications, 2014 Vol.13 No.1/2, pp.56 - 80

Published online: 13 Jul 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article