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Adaptive remediation of the space debris environment using feedback control

Adaptive remediation of the space debris environment using feedback control
Adaptive remediation of the space debris environment using feedback control
In the last few decades, space-related services became a familiar part of everyday life. Moreover, the commercial use of space is forecast to keep increasing in the near future. However, all the spacecraft which provide these services are subject to the risk of being damaged or destroyed by an orbital collision with space debris. To solve the problem, mitigation measures and common standards were defined at industrial, national, and international levels; nevertheless, the number of debris continues to increase. The active removal of space debris was proposed as a solution, and in recent years, several studies addressed the technical and technological challenges needed to realise the first successful mission.

This thesis aims to identify general rules for increasing the effectiveness of active removal strategies in the low Earth orbit. More than 7,000 simulations were run in total, with the effectiveness of traditional post-mission disposal and active debris removal measures evaluated using the normalised effective reduction factor (NERF) as a common metric to quantify the reduction of the population against worst- and best-case scenarios.

This work uses Model to Investigate control Strategies for Space Debris (MISSD), a new sourcesink model of the low Earth orbit (LEO) environment developed in Matlab. Embedded within this model, a feedback controller automatically selects the number and location of objects from two species, inactive payloads, and rocket bodies, to be removed from LEO. Different controls are tested with a proportional, linear, and quadratic laws function of the objects spatial density.

The results demonstrate that it is possible to achieve the same effectiveness with multiple strategies incorporating variations in the post-mission disposal (PMD) compliance and in the number of objects annually removed. The effectiveness of active debris removal (ADR) could be increased by selecting optimal combinations of removals of inactive payloads and rocket bodies. As a general rule, a rise of 30% in PMD compliance produces similar effectiveness to the removal of five additional debris, confirming the primary role of broad adoption of post-mission disposal. For a low number of annual removals, strategies which removed twice the number of rocket bodies compared with the number of inactive payloads results in higher effectiveness, whereas increasing the total annual removal rate and PMD compliance shift the optimal strategy to a more balanced combination of debris species actively removed.

Keywords: space debris, low-Earth orbit, feedback control, adaptive remediation, active debris removal, post-mission disposal
University of Southampton
Somma, Gian Luigi
fe2f9516-1fdb-4b9b-a4ce-7b6a7e60b49a
Somma, Gian Luigi
fe2f9516-1fdb-4b9b-a4ce-7b6a7e60b49a
Lewis, Hugh
e9048cd8-c188-49cb-8e2a-45f6b316336a

Somma, Gian Luigi (2019) Adaptive remediation of the space debris environment using feedback control. University of Southampton, Doctoral Thesis, 166pp.

Record type: Thesis (Doctoral)

Abstract

In the last few decades, space-related services became a familiar part of everyday life. Moreover, the commercial use of space is forecast to keep increasing in the near future. However, all the spacecraft which provide these services are subject to the risk of being damaged or destroyed by an orbital collision with space debris. To solve the problem, mitigation measures and common standards were defined at industrial, national, and international levels; nevertheless, the number of debris continues to increase. The active removal of space debris was proposed as a solution, and in recent years, several studies addressed the technical and technological challenges needed to realise the first successful mission.

This thesis aims to identify general rules for increasing the effectiveness of active removal strategies in the low Earth orbit. More than 7,000 simulations were run in total, with the effectiveness of traditional post-mission disposal and active debris removal measures evaluated using the normalised effective reduction factor (NERF) as a common metric to quantify the reduction of the population against worst- and best-case scenarios.

This work uses Model to Investigate control Strategies for Space Debris (MISSD), a new sourcesink model of the low Earth orbit (LEO) environment developed in Matlab. Embedded within this model, a feedback controller automatically selects the number and location of objects from two species, inactive payloads, and rocket bodies, to be removed from LEO. Different controls are tested with a proportional, linear, and quadratic laws function of the objects spatial density.

The results demonstrate that it is possible to achieve the same effectiveness with multiple strategies incorporating variations in the post-mission disposal (PMD) compliance and in the number of objects annually removed. The effectiveness of active debris removal (ADR) could be increased by selecting optimal combinations of removals of inactive payloads and rocket bodies. As a general rule, a rise of 30% in PMD compliance produces similar effectiveness to the removal of five additional debris, confirming the primary role of broad adoption of post-mission disposal. For a low number of annual removals, strategies which removed twice the number of rocket bodies compared with the number of inactive payloads results in higher effectiveness, whereas increasing the total annual removal rate and PMD compliance shift the optimal strategy to a more balanced combination of debris species actively removed.

Keywords: space debris, low-Earth orbit, feedback control, adaptive remediation, active debris removal, post-mission disposal

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Published date: July 2019

Identifiers

Local EPrints ID: 447843
URI: http://eprints.soton.ac.uk/id/eprint/447843
PURE UUID: 392ba3b0-895b-4c1c-a9c4-efe3fa7fbca0
ORCID for Hugh Lewis: ORCID iD orcid.org/0000-0002-3946-8757

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Date deposited: 24 Mar 2021 17:35
Last modified: 17 Mar 2024 02:44

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Contributors

Author: Gian Luigi Somma
Thesis advisor: Hugh Lewis ORCID iD

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