Learning Chebyshev neural network-based spacecraft attitude tracking control ensuring finite-time prescribed performance

Jia, Qingxian; Li, Genghuan; Yu, Dan; Ahn, Choon Ki; Zhang, Chengxi

doi:10.1016/j.ast.2024.109085

Aerospace Science and Technology

2024

DOI: 10.1016/j.ast.2024.109085

|View full text |Cite

Learning Chebyshev neural network-based spacecraft attitude tracking control ensuring finite-time prescribed performance

Qingxian Jia,

Genghuan Li,

Dan Yu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhancing prescribed-time trajectory tracking control for a stratospheric airship with prescribed performance

SUN,

GUO

et al. 2024

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

Enhancing prescribed-time trajectory tracking control for a stratospheric airship with prescribed performance

SUN,

GUO

et al. 2024

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Li,

Jia,

et al. 2024

AEAT

View full text Add to dashboard Cite

Purpose This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources. Design/methodology/approach The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints. Findings The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy. Practical implications This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications. Originality/value A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Learning Chebyshev neural network-based spacecraft attitude tracking control ensuring finite-time prescribed performance

Cited by 2 publications

References 41 publications

Enhancing prescribed-time trajectory tracking control for a stratospheric airship with prescribed performance

Enhancing prescribed-time trajectory tracking control for a stratospheric airship with prescribed performance

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Contact Info

Product

Resources

About