Dynamical Model Identification for a Small-Scale Unmanned Helicopter Using an Integrated Approach

Abstract

This article presents an integrated approach for the parameter identification of a small-scale unmanned helicopter. With the flight experiment data collection and preprocessing, a hybrid identified algorithm combining the improved artificial bee colony algorithm and prediction error method is proposed to obtain the unknown dynamical parameters of the linear model. The proposed algorithm is valid to use thanks to an adaptive search equation, a novel probability-scaling method, and a chaotic operator and has a good performance in search speed and quality. Afterwards, we design a wind tunnel test to modify the main rotor time constant of the identified model. The identified accuracy and feasibility of the proposed approach are verified by making a time-domain comparison with three other algorithms. Results show that the dynamical characteristics of the helicopter can be determined accurately by the identified model. And the proposed approach is propitious to enhance the reliability and availability of the identified dynamical model.