A Data‐Driven Approach to Integrated Adaptive Morphing and Guidance for Cruise Missiles

To address the complex coupling between aerodynamic characteristics and guidance control for morphing flight missiles, this study proposes a data‐driven approach to integrated adaptive morphing and guidance. Firstly, an aerodynamic surrogate model is constructed using a fully connected neural network (FCNN), mapping the configuration parameters to aerodynamic parameters. Secondly, an adaptive physical parameters optimization network (PPON) is developed to optimize aerodynamic characteristics based on predictions from the aerodynamic surrogate model. Thirdly, an integrated morphing and guidance model is derived by applying the proximal policy optimization (PPO) algorithm from deep reinforcement learning (DRL), embedded with the adaptive aerodynamic optimization model. Eventually, the proposed integrated approach is applied to the guidance task of a morphing cruise missile with variable camber wings. Simulation results demonstrate that the integrated guidance model significantly enhances aerodynamic performance and generates more continuous guidance commands within approximately 4.3 s, outperforming the deep Q‐Network (DQN) algorithm under morphing flight conditions. Moreover, compared to the PPO and DQN‐based guidance laws without morphing flight conditions, the integrated model improves both the guidance accuracy and terminal kinetic energy. Furthermore, the integrated guidance model, trained on stationary targets, remains effective for engaging moving and maneuvering targets, showcasing its robust generalization capability.