Modeling and identification of a small-scale unmanned autonomous helicopter

In this work an identification approach for vertical take-off and landing unmanned aerial vehicles (UAV) in hovering flight is presented. The nonlinear dynamic model is driven from the first principles. The model is then linearized to obtain a linear state-space model presentation of thirteenth order. To identify the unknown parameters, the state-space model has been divided into subsystems. The parameters of the individual subsystems can be determined by applying a suitable identification method such as the prediction-error minimization (PEM) method. A sequential quadratic programming technique (SQP) was used to obtain feasible initial values of the parameters to be identified. Finally, the gained model of the UAV has been validated.