Title

Attractive Manifold-based Noncertainty-equivalence Adaptive Spacecraft Formation Flying Using Output Feedback

Document Type

Conference Proceeding

Publication Date

1-1-2018

Publication Title

AIAA Guidance, Navigation, and Control Conference, 2018

Publisher

American Institute of Aeronautics and Astronautics Inc, AIAA

Issue

210039

Abstract

The paper presents attractive manifold-based noncertainty-equivalence adaptive (NCEA) satellite formation control system for the control of a follower satellite around a leader spacecraft that is orbiting in an elliptic orbit. It is assumed that the follower satellite has unknown mass and is perturbed by unknown periodic and random disturbance forces. The objective is to steer the follower satellite in an specified orbit around the leader spacecraft using feedback of only the output (relative position) measurement. For the purpose of design, first a simplified model including only periodic disturbance inputs is considered. For synthesis using only output variables, a canonical representation of the formation dynamics in an output feedback form is considered, and an estimate of the state variables using certain filters is obtained. Then based on attractive manifold methodology, a nonlinear adaptive noncertainty-equivalence adaptive law is derived. The design is completed in two steps of the backstepping design process. By Lyapunov stability analysis, it is shown that the adaptive law accomplishes global asymptotic tracking of the reference orbit around the leader in the presence of periodic disturbances. For robustness with respect to ran- dom disturbance forces, α-modification is introduced in the adaptation law. Simulation results are presented which show that the designed NCEA output feedback control system achieves precise formation control, despite the periodic and random disturbance forces and parameter uncertainty in the model. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

Language

English

UNLV article access

Share

COinS