Reconfigurable fault-tolerant attitude tracking for spacecraft with unknown nonlinear dynamics using neural network estimators with learning-type weight updating

Qingxian Jia, Chengxi Zhang, Choon Ki Ahn, Jin Wu, Ming Liu

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigated the problem of robust and reconfigurable attitude-tracking control with fault-tolerant capability for spacecraft under nonlinear inertia uncertainties, disturbance torques, and actuator faults. To improve the accuracy of reconstructing actuator faults, we proposed a nonlinear learning neural network estimator that combines the radial basis function neural network (RBFNN) model with an iterative learning algorithm, enabling the arbitrary precision of actuator fault reconstruction. A P-type iterative learning algorithm successively updates the RBFNN’s weight with a low computational load. Moreover, to ensure fast and robust spacecraft attitude fault-tolerant tracking, the learning RBFNN was integrated into a sliding mode control (SMC) scheme, leading to a learning neural-network SMC (LNNSMC), designed using the separation principle. The learning RBFNN was utilized to approximate and compensate for unknown nonlinear attitude dynamics online. Finally, the superiority of the presented method was demonstrated through a numerical example.

Original languageEnglish
Pages (from-to)8213-8227
Number of pages15
JournalNonlinear Dynamics
Volume112
Issue number10
DOIs
Publication statusPublished - 2024 May

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature B.V. 2024.

Keywords

  • Learning neural network estimator
  • RBFNN model
  • Reconfigurable fault-tolerant control
  • Spacecraft attitude tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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