Abstract:

 A method based on sparse difference and mesh refinement is proposed for hypersonic glide reentry trajectory optimization. The method converts the trajectory optimization into the nonlinear programming (NLP) using the local collocation method and the efficiency and accuracy are improved in two aspects. On the one hand, an efficient sparse difference method is introduced to calculate the first-order partial derivatives of the NLP functions so that the NLP can be solved efficiently. On the other hand, an adaptive mesh refinement method is proposed to refine the mesh so that the high accuracy can be achieved with a smaller mesh size, thereby reducing the NLP size and the computational load. The proposed method is applied to the maximum cross range optimization of a hypersonic glide reentry vehicle. The numerical results show that the method can generate an optimal three-dimensional reentry trajectory satisfying all constraints rapidly. Furthermore, the landing footprint of the hypersonic glide reentry vehicle is studied using the method, which further demonstrates the effectiveness of the method.

Key words:  Hypersonic, Trajectory optimization, Sparse finite difference, Mesh refinement, Landing footprint