Event-Triggered Adaptive Neural Network Backstepping Sliding Fault-Tolerant Control of Spacecraft Formation Flying With Input Saturation

This study explores the challenge of tracking control for spacecraft formation flying (SFF) in the presence of dynamic uncertainties and external perturbations. Firstly, sliding mode control combined with backstepping control is used to address saturation issues. Then, neural networks, minimal parameter learning, and adaptive control are integrated to handle dynamic uncertainties and actuator failures. To alleviate the communication load, an event-triggered mechanism is ultimately implemented, which leads to the development of an adaptive sliding mode fault-tolerant control algorithm based on an event-triggered neural network. This control architecture achieves significant advancements over traditional techniques: (1) ensuring system robustness and adaptability in complex scenarios with uncertain system dynamics and external disturbances, effectively counteracting actuator failures and input saturation issues; (2) significantly reducing transmission and computational burdens in resource-limited networked systems through the adoption of event-triggered control (ETC) mechanisms; (3) achieving high-precision tracking performance for SFF without relying on prior knowledge of the system's inherent dynamics, environmental disturbances, or potential actuator deficiencies. The Lyapunov approach is utilized to confirm the closed-loop system's boundedness. Finally, the proposed method's efficacy is confirmed via simulations with a two-satellite formation.