Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Real-Time Neural Network Based Online Identification Technique for a UAV Platform
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
Real-time Neural Network based Identification of a Rotary-Wing UAV dynamics for autonomous flight
ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
Integrated identification modeling of rotorcraft-based unmanned aerial vehicle
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
System identification and attitude control of a small scale unmanned helicopter
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
Modeling of unmanned small scale rotorcraft based on Neural Network identification
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Signal Processing
Neurocontrol: A literature survey
Mathematical and Computer Modelling: An International Journal
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
IEEE Transactions on Neural Networks
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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.