Artificial Intelligence Based Spacecraft Resilience Optimization in Space Informatics Digital Twins

Abstract

This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial informatics and digital twins technology, this work initially employs AI techniques, specifically the radial basis function neural networks, a deep learning algorithm, to perform global optimization and orbit fitting for spacecraft. Augmented Lagrangian multipliers are then introduced to locally optimize this neural network. Additionally, to further enhance the spacecraft's flexibility, an improved particle swarm optimization (PSO) algorithm is applied to optimize the proposed network. The work also introduces a periodic variational multiobjective quantum particle swarm optimization (PMQPSO) algorithm. Subsequently, a rigid-flexible coupled dynamics model for the spacecraft is established, and relevant simulations and experiments are conducted to support this work. The results indicate that the average fitness of the improved PMQPSO algorithm decreases to 18.23 after 500 iterations, with its performance being at least 3.2% higher than that of the classical quantum PSO algorithm. Furthermore, after the initial decline in the first order, the limiter residuals no longer decline and exhibit convergence, as the residual curve transitions from high to low, indicating a gradual improvement in convergence and stability. These findings highlight the advantages of the PMQPSO algorithm in optimizing the spacecraft's elasticity. In conclusion, this parameter optimization holds practical significance for the design optimization of aircraft aerodynamic shapes.