Deep learning-enhanced prescribed-time cooperative guidance with switching topologies under time-varying velocity

This paper addresses the prescribed-time cooperative guidance problem for striking a target under time-varying velocity, leveraging deep neural networks to enhance prediction accuracy. Unlike existing results, the proposed guidance law guarantees the stability of the guidance error system even under switching topologies. First, a 3-D vector-based cooperative guidance model is established, and the cooperative guidance objective is formulated. To achieve precise time-to-go estimation under time-varying velocity, a high-precision prediction algorithm based on deep neural networks is developed. Building on this, a practical prescribed-time cooperative guidance law accounting for time-varying velocity is designed, with rigorous stability analysis provided for the guidance error system under switching topologies. Furthermore, based on guidance trajectory analysis, 2-D results in both horizontal and vertical planes are provided. Finally, the proposed method is validated through numerical simulations and equivalent physical experiments.