Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition

Aitor Ramirez Gomez; Mohamad Al Ahdab

doi:10.1016/j.ifacol.2023.10.646

Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition

Aitor Ramirez Gomez, Mohamad Al Ahdab

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

1 Citation (Scopus)

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Abstract

This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors

Original language	English
Book series	IFAC-PapersOnLine
Volume	56
Issue number	2
Pages (from-to)	6012-6017
Number of pages	6
ISSN	1474-6670
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.646
Publication status	Published - 1 Jul 2023
Event	22nd IFAC World Congress 2023 - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 https://www.ifac2023.org/

Conference

Conference	22nd IFAC World Congress 2023
Country/Territory	Japan
City	Yokohama
Period	09/07/2023 → 14/07/2023
Internet address	https://www.ifac2023.org/

Keywords

Decision making and autonomy
Extremum seeking and model free adaptive control
Game theory
High accuracy pointing
LISA
Satellite constellation
Space exploration

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10.1016/j.ifacol.2023.10.646Licence: CC BY-NC-ND 4.0

Open Access articleFinal published version, 759 KBLicence: CC BY-NC-ND 4.0

https://arxiv.org/pdf/2303.02743.pdf

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Cite this

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abstract = "This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors ",

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AB - This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors

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KW - Extremum seeking and model free adaptive control

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KW - High accuracy pointing

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KW - Satellite constellation

KW - Space exploration

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Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition

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