  In order to achieve a good level of autonomy in unmanned helicopters, an
accurate replication of vehicle dynamics is required, which is achievable
through precise mathematical modeling. This paper aims to identify a parametric
state-space system for an unmanned helicopter to a good level of accuracy using
Invasive Weed Optimization (IWO) algorithm. The flight data of Align TREX 550
flybarless helicopter is used in the identification process. The rigid-body
dynamics of the helicopter is modeled in a state-space form that has 40
parameters, which serve as control variables for the IWO algorithm. The results
after 1000 iterations were compared with the traditionally used Prediction
Error Minimization (PEM) method and also with Genetic Algorithm (GA), which
serve as references. Results show a better level of correlation between the
actual and estimated responses of the system identified using IWO to that of
PEM and GA.
