Comparison of Real-time Online and Offline Neural Network Models for a UAV

In this paper a comparison of an offline and online neural network architecture for the identification of an unmanned aerial vehicle (UAV) is presented. The identification algorithm is based on autoregressive model aided by neural networks for the six degree of freedom, non-linear dynamics of a fixed wing UAV. One of the architectures involved the use of a single network to model the complete UAV system and the other involved the use of two decoupled networks for the lateral and longitudinal dynamics taking coupling into account. Numerical simulation results are presented for each of these architectures. The results have been validated using the real-time hardware in the loop (HIL) simulation technique for different sets of flight data.