SAAB 340B aerodynamic model development using binary particle swarm optimization

Abstract

his paper follows-up previous work on the development of a high-fidelity Saab 340B aerodynamic model using system identification methods. In the prior work, Saab 340B flight tests were carried out using different excitations on the control surfaces. The flight test data was collected at predefined trim points. Thrust forces and moment were obtained using the propeller efficiency map provided by the manufacturer. The equation and output error methods were employed to analyse flight test data to estimate aerodynamic parameters in the time domain. This paper follows-up previous work on the development of a high-fidelity Saab 340B aerodynamic model using system identification methods. In the prior work, Saab 340B flight tests were carried out using different excitations on the control surfaces. The flight test data was collected at predefined trim points. Thrust forces and moment were obtained using the propeller efficiency map provided by the manufacturer. The equation and output error methods were employed to analyse flight test data to estimate aerodynamic parameters in the time domain. The paper extends the work to select independent variables in the equation error method in an optimal way using binary particle swarm to determine the best subset of independent variables. The impact of the hyperparameters of the binary PSO approach such as the transfer function scheme, inertia weight updating strategy, and the value of acceleration coefficients is investigated.