2007
DOI: 10.2514/1.21433
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Optimal Multi-Objective Linearized Impulsive Rendezvous

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Cited by 88 publications
(49 citation statements)
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“…Introduction C ONTROL of space vehicles for proximity operations, such as rendezvous and docking, is a problem of great interest for future manned missions [1], autonomous guidance of satellite swarms [2], onorbit servicing missions operated by free-flyer robotic spacecraft for refueling, monitoring or removing satellites [3][4][5], or for the autonomous onorbit assembly of a large space structure [6].…”
mentioning
confidence: 99%
“…Introduction C ONTROL of space vehicles for proximity operations, such as rendezvous and docking, is a problem of great interest for future manned missions [1], autonomous guidance of satellite swarms [2], onorbit servicing missions operated by free-flyer robotic spacecraft for refueling, monitoring or removing satellites [3][4][5], or for the autonomous onorbit assembly of a large space structure [6].…”
mentioning
confidence: 99%
“…C-W equations, derived by Clohessey and Wiltshire in 1960 [5], have been widely used to describe the linear relative motion between two neighboring spacecraft if the target orbit is approximately circular and the distance between them is much smaller than the orbit radius. For example, [13], [18] and [25] studied the optimal impulsive rendezvous problem based on C-W equations, and [17] utilized annealing algorithm to design orbital controllers for the rendezvous model depicted by C-W equations. In recent years, with the development of control theory, many advanced methods have been utilized to solve the rendezvous control problem.…”
Section: Introductionmentioning
confidence: 99%
“…The non-dominated sorting genetic algorithm (NSGA) is one of the common methods. However, the drawbacks are obvious, such as the high computational complexity of ) O MN , which resulted in better performance than NSGA [25,26]. This paper studied the multi-objective optimal trajectory planning of the operational robot based on NSGA-2.…”
Section: Multi-objective Optimal Trajectory Planning Algorithmmentioning
confidence: 99%