Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

作     者：Jiang Tieying Li Jie Huang Kewei 

作者机构：College of Mechanical and Electrical EngineeringBeijing Institute of Technology 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2015年第28卷第3期

页      面：865-873页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 082503[工学-航空宇航制造工程] 0802[工学-机械工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Defense Basic Research Program of China(No.B22201320xx) 

主　　题：Aerodynamic parameters Local optimization Parameter identification Particle swarm optimization(PSO) Small unmanned aerialvehicle 

摘      要：Abstract This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle （UAV） through modified particle swam optimization （PSO）. The proce- dure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems （MEMS） inertial mea,mring element and a global positioning system （GPS） receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration （SRPSO）. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.