Hp-Adaptive Dynamic Trust Region Sequential Convex Programming: An Enhanced Strategy for Trajectory Optimization

Under stringent flight constraint conditions, trajectory optimization of Hypersonic Vehicles (HV) is crucial for guidance. Therefore, this paper proposes an hp-adaptive dynamic trust region SCP method to address the problems of initial value sensitivity and low computational efficiency that arise in traditional Sequential Convex Programming (SCP) methods during HV trajectory optimization. First, the discrete symmetry principle is utilized to simplify the boundary conditions and constraint handling of the trajectory model while combining scale transformation invariance to achieve nondimensionalization of the model’s physical quantities, thereby constructing the trajectory optimization model. On this basis, the hp-adaptive method is adopted to dynamically adjust the discretization step size and polynomial approximation order, and combined with an adaptive adjustment strategy for the trust region radius, to improve computational efficiency while ensuring optimization accuracy. Finally, the effectiveness of the algorithm is validated through optimizing the gliding phase of HV, and compared with GPOPS-II and fixed trust region SCP methods. The experimental results show that the algorithm has superiority in improving convergence speed, computational efficiency and solution accuracy, with its performance significantly outperforming traditional trajectory optimization schemes.