  The complexity of helicopter flight dynamics makes modeling and helicopter
system identification a very difficult task. Most of the traditional techniques
require a model structure to be defined apriori and in case of helicopter
dynamics, this is difficult due to its complexity and the interplay between
various subsystems.To overcome this difficulty, non-parametric approaches are
commonly adopted for helicopter system identification. Artificial Neural
Network are a widely used class of algorithms for non-parametric system
identification, among them, the Nonlinear Auto Regressive eXogeneous input
network (NARX) model is very popular, but it also necessitates some in depth
knowledge regarding the system being modeled. There have been many approaches
proposed to circumvent this and yet still retain the advantageous
characteristics. In this paper we carry out an extensive study of one such
newly proposed approach using a modified NARX model with a two tiered,
externally driven recurrent neural network architecture. This is coupled with
an outer optimization routine for evolving the order of the system. This
generic architecture is comprehensively explored to ascertain its usability and
critically asses its potential. Different instantiations of this architecture,
based on nature inspired computational techniques (Artificial Bee Colony,
Artificial Immune System and Particle Swarm Optimization) are evaluated and
critically compared in this paper. Simulations have been carried out for
identifying the longitudinally uncoupled dynamics. Results of identification
indicate a quite close correlation between the actual and the predicted
response of the helicopter for all the models.
