Multi-constraint Online Guidance Method Based on Meta-learning

Chao Li; Fenfen Xiong; Yue Zhao

doi:10.1007/978-981-19-6613-2_226

Multi-constraint Online Guidance Method Based on Meta-learning

Chao Li, Fenfen Xiong^*, Yue Zhao

^*Corresponding author for this work

School of Aerospace Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, an online guidance method based on the few-shot learning theory is proposed. First of all, a muti-constrained optimal control model is constructed for the guidance problem. Secondly, through using the meta-learning theory in the field of few-shot learning, a highly reliable and non-iterative online guidance method considering multiple constraints that can quickly adapt to a variety of guidance problems of the same category is developed. The effectiveness of the proposed method is verified by numerical simulations.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
Editors	Liang Yan, Haibin Duan, Yimin Deng, Liang Yan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	2320-2331
Number of pages	12
ISBN (Print)	9789811966125
DOIs	https://doi.org/10.1007/978-981-19-6613-2_226
Publication status	Published - 2023
Event	International Conference on Guidance, Navigation and Control, ICGNC 2022 - Harbin, China Duration: 5 Aug 2022 → 7 Aug 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	845 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2022
Country/Territory	China
City	Harbin
Period	5/08/22 → 7/08/22

Keywords

Deep learning
Few-shot learning
Meta-learning
Multi-constraint
Online guidance

Access to Document

10.1007/978-981-19-6613-2_226

Cite this

Li, C., Xiong, F., & Zhao, Y. (2023). Multi-constraint Online Guidance Method Based on Meta-learning. In L. Yan, H. Duan, Y. Deng, & L. Yan (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control (pp. 2320-2331). (Lecture Notes in Electrical Engineering; Vol. 845 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6613-2_226

Li, Chao ; Xiong, Fenfen ; Zhao, Yue. / Multi-constraint Online Guidance Method Based on Meta-learning. Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. editor / Liang Yan ; Haibin Duan ; Yimin Deng ; Liang Yan. Springer Science and Business Media Deutschland GmbH, 2023. pp. 2320-2331 (Lecture Notes in Electrical Engineering).

@inproceedings{cf9d161acc984421830582ff56620f33,

title = "Multi-constraint Online Guidance Method Based on Meta-learning",

abstract = "In this study, an online guidance method based on the few-shot learning theory is proposed. First of all, a muti-constrained optimal control model is constructed for the guidance problem. Secondly, through using the meta-learning theory in the field of few-shot learning, a highly reliable and non-iterative online guidance method considering multiple constraints that can quickly adapt to a variety of guidance problems of the same category is developed. The effectiveness of the proposed method is verified by numerical simulations.",

keywords = "Deep learning, Few-shot learning, Meta-learning, Multi-constraint, Online guidance",

author = "Chao Li and Fenfen Xiong and Yue Zhao",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",

year = "2023",

doi = "10.1007/978-981-19-6613-2_226",

language = "English",

isbn = "9789811966125",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "2320--2331",

editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",

booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",

address = "Germany",

}

Li, C, Xiong, F & Zhao, Y 2023, Multi-constraint Online Guidance Method Based on Meta-learning. in L Yan, H Duan, Y Deng & L Yan (eds), Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. Lecture Notes in Electrical Engineering, vol. 845 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 2320-2331, International Conference on Guidance, Navigation and Control, ICGNC 2022, Harbin, China, 5/08/22. https://doi.org/10.1007/978-981-19-6613-2_226

Multi-constraint Online Guidance Method Based on Meta-learning. / Li, Chao; Xiong, Fenfen; Zhao, Yue.
Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. ed. / Liang Yan; Haibin Duan; Yimin Deng; Liang Yan. Springer Science and Business Media Deutschland GmbH, 2023. p. 2320-2331 (Lecture Notes in Electrical Engineering; Vol. 845 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-constraint Online Guidance Method Based on Meta-learning

AU - Li, Chao

AU - Xiong, Fenfen

AU - Zhao, Yue

PY - 2023

Y1 - 2023

N2 - In this study, an online guidance method based on the few-shot learning theory is proposed. First of all, a muti-constrained optimal control model is constructed for the guidance problem. Secondly, through using the meta-learning theory in the field of few-shot learning, a highly reliable and non-iterative online guidance method considering multiple constraints that can quickly adapt to a variety of guidance problems of the same category is developed. The effectiveness of the proposed method is verified by numerical simulations.

AB - In this study, an online guidance method based on the few-shot learning theory is proposed. First of all, a muti-constrained optimal control model is constructed for the guidance problem. Secondly, through using the meta-learning theory in the field of few-shot learning, a highly reliable and non-iterative online guidance method considering multiple constraints that can quickly adapt to a variety of guidance problems of the same category is developed. The effectiveness of the proposed method is verified by numerical simulations.

KW - Deep learning

KW - Few-shot learning

KW - Meta-learning

KW - Multi-constraint

KW - Online guidance

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U2 - 10.1007/978-981-19-6613-2_226

DO - 10.1007/978-981-19-6613-2_226

M3 - Conference contribution

AN - SCOPUS:85151150431

SN - 9789811966125

T3 - Lecture Notes in Electrical Engineering

SP - 2320

EP - 2331

BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control

A2 - Yan, Liang

A2 - Duan, Haibin

A2 - Deng, Yimin

A2 - Yan, Liang

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022

Y2 - 5 August 2022 through 7 August 2022

ER -

Li C, Xiong F, Zhao Y. Multi-constraint Online Guidance Method Based on Meta-learning. In Yan L, Duan H, Deng Y, Yan L, editors, Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. Springer Science and Business Media Deutschland GmbH. 2023. p. 2320-2331. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-19-6613-2_226

Multi-constraint Online Guidance Method Based on Meta-learning

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Keywords

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