Online corrections to neural policy guidance for pinpoint powered descent

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dc.contributor.authorCho, Namhoon
dc.contributor.authorShin, Hyo-Sang
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorAmato, Davide
dc.date.accessioned2024-02-06T16:58:09Z
dc.date.available2024-02-06T16:58:09Z
dc.date.issued2024-02-03
dc.description.abstractThis study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearize the dynamics around the baseline values of its arguments and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, in other words, the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be used as a means to compensate for the prediction errors of pretrained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. A numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem on Mars.en_UK
dc.identifier.citationCho N, Shin HS, Tsourdos A, Amato D. (2024) Online corrections to neural policy guidance for pinpoint powered descent. Journal of Guidance, Control, and Dynamics, Volume 47, Issue 5, May 2024, pp. 945-963en_UK
dc.identifier.issn0731-5090
dc.identifier.urihttps://doi.org/10.2514/1.G007234
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20755
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleOnline corrections to neural policy guidance for pinpoint powered descenten_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-12-05

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