4.6 Article

Optimal reconfigurations of two-craft Coulomb formations along manifolds

期刊

ACTA ASTRONAUTICA
卷 83, 期 -, 页码 108-118

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actaastro.2012.10.028

关键词

Coulomb formations; Invariant manifolds; Optimal reconfigurations; Particle swarm optimization

资金

  1. Department of Defense's National Defense Science and Engineering Graduate Fellowship

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Coulomb formations refer to swarms of closely flying spacecraft, in which the net electric charge of each vehicle is controlled. Active charge control is central to this concept and enables a propulsion system with highly desirable characteristics, albeit with limited controllability. Numerous Coulomb formation equilibria have been derived, but to maintain and maneuver these configurations, some inertial thrust is required to supplement the nearly propellant-less charge control. In this work, invariant manifold theory is applied to two-craft Coulomb equilibria, which are admitted in a linearized two-body gravity model. The manifolds associated with these systems are analyzed for the first time, and are then utilized as part of a general procedure for formulating optimal reconfigurations. Specifically, uncontrolled flows along the manifolds are sought which provide near continuous transfers from one equilibrium to another. Control is then introduced to match continuity, while minimizing inertial thrusting. This methodology aims to exploit uncontrolled motions and charge control to realize the shape-changing ability of these formations, without large inertial control efforts. Some variations in formulating and parameterizing the optimal transfers are discussed, and analytical expressions are derived to aid in establishing control parameter limits, under certain assumptions. Numerical results are provided, as demonstrative examples of the optimization procedure, using relatively simple control approximations. Finally, Particle Swarm Optimization, a novel stochastic method, is used with considerable success to solve the numerically difficult parameter optimization problems. (C) 2012 Elsevier Ltd. All rights reserved.

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