Adaptive Learning Control-Based Periodic Trajectory Tracking for Spacecraft Formations

Hong Wong, Vikram Kapila

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses a periodic trajectory tracking problem arising in spacecraft formation flying. In particular, the nonlinear position dynamics of a follower spacecraft relative to a leader spacecraft are utilized to develop a learning controller which learns a periodic, unknown model reference control. Using a Lyapunov-based approach, a full state feedback control law, a parameter update algorithm, and a model reference control estimate are designed that facilitate the tracking of given periodic reference trajectories in the presence of unknown leader and follower spacecraft masses. Furthermore, using a discrete Lyapunov-type stability analysis, model reference control error is shown to converge to zero. Illustrative simulations are included to demonstrate the efficacy of the proposed controller.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Pages3597-3602
Number of pages6
DOIs
StatePublished - Dec 1 2003
Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
Duration: Dec 9 2003Dec 12 2003

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume4
ISSN (Print)0191-2216

Other

Other42nd IEEE Conference on Decision and Control
CountryUnited States
CityMaui, HI
Period12/9/0312/12/03

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Wong, H., & Kapila, V. (2003). Adaptive Learning Control-Based Periodic Trajectory Tracking for Spacecraft Formations. In Proceedings of the IEEE Conference on Decision and Control (pp. 3597-3602). (Proceedings of the IEEE Conference on Decision and Control; Vol. 4). https://doi.org/10.1109/CDC.2003.1271706