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Strong Tracking Sigma Point Predictive Variable Structure Filter for Attitude Synchronisation Estimation

Published online by Cambridge University Press:  22 January 2018

Lu Cao*
Affiliation:
(National Institute of Defense Technology Innovation, Academy of Military Sciences PLA China, Beijing, China) (School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China)
Dong Qiao
Affiliation:
(School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China)
Han Lei
Affiliation:
(State Key Laboratory of Astronautic Dynamics, China Xi'an Satellite Control Center, Xi'an 710043, China)
Gongbo Wang
Affiliation:
(Astronaut Center of China, No. 26 Beiqing Street, Haidian District, Beijing, China)

Abstract

In this paper, a novel Strong Tracking Sigma-Point Predictive Variable Structure Filter (ST-SP-PVSF) is presented as a further development of the Adaptive Predictive Variable Structure Filter (APVSF) for attitude synchronisation during Satellite Formation Flying (SFF). First, the sequence orthogonal principle is adopted to enhance the robustness of the APVSF for any nonlinear system with uncertain model errors. Then, sigma-point sampling strategies (such as unscented transfer, cubature rule and Stirling's polynomial interpolation) are introduced to extend the APVSF with the ability to capture the second central moment's information on the model errors to update the system model with higher precision. The new methodology has advantages in dealing with the various types of uncertainties or model errors compared with the APVSF. In addition, it does not need to choose the limit boundary layer ψlim it for system estimation, which reduces the sensitivity to the initial parameters and improves its adaptive ability over the APVSF. Simulations are performed to demonstrate that the proposed method is more suitable for attitude synchronisation estimation of the SFF system.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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