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Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming

Sagliano, Marco and Seelbinder, David and Theil, Stephan (2023) Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming. In: Autonomous Dynamic Trajectory Optimal Control of Launch Vehicles Springer.

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Abstract

Abstract this chapter expands our development of an autonomous descent guidance algorithm which is able to deal with both the aerodynamic descent and the powered landing phases of a reusable rocketN the method uses sequential convex optimization applied to a cartesian representation of the equations of motionL and the transcription is based on the use of hp pseudospectral methodsN the major contributions of the formulation are a more systematic exploitation and separation of convex and nonM convex contributions to minimize the computation of the latterL the inclusion of highly nonlinear terms represented by aerodynamic accelerationsL a complete reformulation of the problem based on the use of euler angle rates as control meansL an improved transcription based on the use of a generalized hp pseudospectral methodL and a dedicated formulation of the aerodynamic guidance problem for reusable rocketsN the approach is demonstrated for a TP knMclass reusable rocketN numerical results confirm that the methodology we propose is very effective and able to satisfy all the constraints acting on the systemN it is therefore a valid candidate solution to solve the entire descent phase of reusable rockets in realMtimeN the last ten years have been disruptive for rocket technologyN we are witnessing a paradigm shift which has its focus on reusabilityL a dream pursued since the beginning of the space shuttle program [X]L but that only now we are able to fully see as weeklyMbasedL operative technologyN this is mainly the result of spacex effortsN the company led by elon musk paved the way for a deep reshaping of the conception of rocketsL mainly with their falcon Y programL ableL at the moment that this chapter is getting writtenL to successfully complete its 1PP th landing [RV]N the concurrent development of the even more ambitious starship program [RW]L together with the efforts of other playersL such as rocket lab with its neutron [Y] and blue origin with the new glenn rocket [1V] confirms that the disruption we are experiencing is irreversibleL and needs to be embraced rather than fearedN with this spirit agencies and intergovernmental institutions are updating their plans to keep the pace of the private sector.

Item URL in elib:https://elib.dlr.de/187039/
Document Type:Book Section
Title:Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sagliano, MarcoMarco.Sagliano (at) dlr.dehttps://orcid.org/0000-0003-1026-0693
Seelbinder, Daviddavid.seelbinder (at) dlr.dehttps://orcid.org/0000-0003-4080-3169
Theil, Stephanstephan.theil (at) dlr.dehttps://orcid.org/0000-0002-5346-8091
Date:March 2023
Journal or Publication Title:Autonomous Dynamic Trajectory Optimal Control of Launch Vehicles
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Publisher:Springer
Status:Accepted
Keywords:Launch vehicles, Reusable rockets, trajectory optimization
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transportation
DLR - Research area:Raumfahrt
DLR - Program:R RP - Space Transportation
DLR - Research theme (Project):R - Project CALLISTO [RP]
Location: Bremen
Institutes and Institutions:Institute of Space Systems > Navigation and Control Systems
Deposited By: Sagliano, Marco
Deposited On:27 Jun 2022 12:14
Last Modified:27 Jun 2022 12:14

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