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

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

Research output: Contribution to journalArticlepeer-review

Abstract

This article presents a finite-time prescribed performance (FTPP) control approach based on a learning Chebyshev neural network (LCNN) for spacecraft attitude tracking with modeling uncertainties, actuator faults, and external disturbances. An FTPP function is designed to specify the desired accuracy boundary and finite-time convergence. Further, an FTPP-based learning sliding mode controller (LSMC) is constructed, where the lumped disturbance is approximated and compensated via a novel LCNN model. Unlike conventional adaptive CNN models, the LCNN model employs an iterative learning mechanism for adjusting the weights of the CNN model, reducing computing costs. The FTPP-based LSMC approach is presented with a detailed stability analysis. The proposed method offers a broad range of applications with the FTPP criteria satisfied. A series of simulations are performed to verify the validity and applicability of the proposed approach.

Original languageEnglish
Article number109085
JournalAerospace Science and Technology
Volume148
DOIs
Publication statusPublished - 2024 May

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Masson SAS

Keywords

  • Chebyshev neural network
  • Finite-time prescribed performance control
  • Learning sliding mode control
  • Spacecraft attitude tracking

ASJC Scopus subject areas

  • Aerospace Engineering

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