Online Trajectory Optimization Based on Pseudospectra Convex Optimization for Morphing Gliding Reentry Vehicles

Trajectory planning for morphing gliding reentry vehicles is a nonconvex optimization problem driven by nonlinearity, parameter uncertainty, and multiple constraints. No-fly zones (NFZs) are a critical constraint because their rapid movement and expansion hinder the real-time generation of optimal flight trajectories and wing morphing strategies. Therefore, this study proposes an innovative online trajectory optimization method based on sequential convex optimization integrated with a deep neural network (DNN). The proposed method first uses the Radau pseudospectral method to discretize continuous dynamics and convert the non-convex trajectory planning problem into a relaxed convex subproblem. The subproblem is reformulated as an augmented Lagrangian function through linearization and is iteratively solved using the interior-point method. Finally, the DNN learns the mapping between flight states and optimal control variables (angle of attack rate, bank angle rate, and wing sweep angle rate) to rapidly generate control variables. Different from the time-consuming offline optimization method, the proposed model only requires 0.4 ms to predict three groups of control variables, with the predicted control errors remaining below 2.25%. This method efficiently provides high-precision and stable reentry trajectories and morphing strategies for gliding reentry vehicles. Thus, the proposed method achieves synchronous flight path and wing deformation optimization and demonstrates strong robustness under time-varying mission conditions.