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

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.