Comparative study on dynamic identification of parallel motion platform for a novel flight simulator

Author

Wu, Dongsu ; Gu, Hongbin ; Li, Peng

Author_Institution

Coll. of Civil Aviation, Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China

fYear

2009

Firstpage

2232

Lastpage

2237

Abstract

This paper investigates theoretical and experimental comparison of LS estimation and Bayesian type filters such as EKF, UKF, PF and GMSPPF (Gaussian Mixture Sigma Point Particle Filter) methods for dynamic identification of a 6-DOF parallel motion platform. Comparison results show that the UKF method and the GMSPPF method are most efficient and easy-to-use parameter identification approaches for highly nonlinear system.

Keywords

Kalman filters; aerospace simulation; least squares approximations; motion control; nonlinear control systems; parameter estimation; Bayesian type filters; Gaussian mixture sigma point particle filter; extended Kalman filter; flight simulator; least squares estimation; nonlinear system; parallel motion platform; parameter identification approach; particle filter; unscented Kalman filter; Aerodynamics; Aerospace simulation; Costs; Error correction; Least squares approximation; Manipulator dynamics; Nonlinear dynamical systems; Parallel robots; Parameter estimation; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on

Conference_Location

Guilin

Print_ISBN

978-1-4244-4774-9

Electronic_ISBN

978-1-4244-4775-6

Type

conf

DOI

10.1109/ROBIO.2009.5420471

Filename

5420471