Hybrid DE-PEM algorithm for identification of UAV helicopter

Abstract

Purpose: The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model. Design/methodology/approach: In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis. Findings: The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes. Research limitations/implications: This study is applicable to only linearized state-space model. Practical implications: The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development. Originality/value: This study presents a novel hybrid algorithm for system identification of an autonomous helicopter model.