Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

Ezabadi, Mostafa; Zahmatkesh, Mohsen; Emami, Seyyed Ali; Castaldi, Paolo

Abstract

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Citation

Ezabadi, M., Zahmatkesh, M., Emami, S. A., & Castaldi, P. (2025). Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning. Advances in Space Research, 75(1), 1231-1244. https://doi.org/10.1016/j.asr.2024.09.041

• Journal Article Type: Article
• Acceptance Date: Sep 21, 2024
• Online Publication Date: Sep 26, 2024
• Publication Date: Jan 1, 2025
• Deposit Date: Dec 31, 2024
• Journal: Advances in Space Research
• Print ISSN: 0273-1177
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 75
• Issue: 1
• Pages: 1231-1244
• DOI: https://doi.org/10.1016/j.asr.2024.09.041
• Public URL: https://durham-repository.worktribe.com/output/3230272