State-Space Identification of Unmanned Helicopter Dynamics using Invasive Weed Optimization Algorithm on Flight Data
This work addresses the need for precise mathematical modeling to improve autonomy in unmanned helicopters, representing an incremental improvement over existing optimization techniques.
The paper tackled the problem of accurately modeling unmanned helicopter dynamics by using the Invasive Weed Optimization algorithm on flight data, achieving better correlation between actual and estimated responses compared to traditional methods like Prediction Error Minimization and Genetic Algorithm.
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.