Abstract:To address the issue of spacecraft formation maintenance under communication constraints and space disturbances, this paper investigates a control method based on the learning Chebyshev Neural Network (CNN) and a Dynamic Event Trigger (DET) mechanism. To compensate for the effects of space disturbances, a learning CNN model based on an iterative learning algorithm is proposed, which requires fewer computational resources compared to traditional CNN models. Subsequently, a terminal sliding mode controller is designed based on sliding mode control theory and graph theory. Finally, a DET mechanism is designed for the proposed controller. Simulation results demonstrate that the proposed control method achieves high-precision formation maintenance, effectively reduces inter-formation communication frequency by over 92.01%, and avoids the Zeno phenomenon.