Work

Neural network-based fixed-time practical attitude synchronization control for uncertain networked spacecraft systems

Year: 2025

Type: article

Abstract: This paper investigates the distributed neural network (NN)-based fixed-time practical attitude synchronization of the networked spacecraft systems (NSSs) with model uncertainties and external disturbances. A novel practical attitude synchronization scheme is proposed using the Lagrangian representation of spacecraft attitude dynamics for both leaderless and leader-follower cases. Fixed-time control is first employed to enhance robustness against model uncertainties, and the NN is then integrated to advance its adaptability. Additionally, a unified analytical framework is developed to establish the fixed-time practical stability of the control system using the Lyapunov function method. Furthermore, the allowable upper bound expressions of the practical attitude synchronization error and the settling time are analytically derived for the NSSs. Finally, comparative simulations are conducted to validate the feasibility and effectiveness of the proposed control scheme.

Source: ISA Transactions

DOI: 10.1016/j.isatra.2025.08.005

Authors: Runlong Peng, Jinchen Ji, Bin Zheng, Nan Li, Zhonghua Miao, Jin Zhou

Institutions: Shanghai University, University of Technology Sydney

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,150

Funders: National Natural Science Foundation of China, Shanghai Municipal Education Commission