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

State-Space Identification of Unmanned Helicopter Dynamics using Invasive Weed Optimization Algorithm on Flight Data