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A Study on Coaxial Quadrotor Model Parameter Estimation: an Application of the Improved Square Root Unscented Kalman Filter

  • Jarosław Gośliński
  • Andrzej Kasiński
  • Wojciech Giernacki
  • Piotr Owczarek
  • Stanisław Gardecki
Open Access
Article
  • 94 Downloads

Abstract

The parametrized model of the Unmanned Aerial Vehicle (UAV) is a crucial part of control algorithms, estimation processes and fault diagnostic systems. Among plenty of available methods for model structure or model parameters estimation, there are a few, which are suitable for nonlinear UAV models. In this work authors propose an estimation method of parameters of the coaxial quadrotor’s orientation model, based on the Square Root Unscented Kalman Filter (SRUKF). The model structure with different aerodynamic aspects is presented. The model is enhanced with various friction types, so it reflects the real quadrotor characteristics more precisely. In order to validate the estimation method, the experiments are conducted in a special hall and essential data is gathered. The research shows that the SRUKF, can provide fast and reliable estimation of the model parameters, however the classic method may lead to serious instabilities. Necessary modifications of the estimation algorithm are included, so the approach is more robust in terms of numerical stability. The resultant method allows for dynamics of selected parameters to be changed and is proved to be adequate for on-line estimation. The studies reveals tracking properties of the algorithm, which makes the method more viable.

Keywords

Parameter estimation Coaxial quadrotor Mathematical model Nonlinear filtration Square root unscented Kalman filter 

Notes

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.AISENS Sp. z o.o.PoznanPoland
  2. 2.Institute of Control, Robotics and Information Engineering, Faculty of Electrical EngineeringPoznan University of TechnologyPoznanPoland
  3. 3.Institute of Mechanical Technology, Faculty of Mechanical Engineering and ManagementPoznan University of TechnologyPoznanPoland

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