TY - JOUR
T1 - Multitask-constrained reentry trajectory planning for hypersonic gliding vehicle
AU - Huang, Ang
AU - Yu, Jianglong
AU - Liu, Yumeng
AU - Hua, Yongzhao
AU - Dong, Xiwang
AU - Ren, Zhang
N1 - Publisher Copyright:
© 2024 Elsevier Masson SAS
PY - 2024/12
Y1 - 2024/12
N2 - This paper studies the reentry trajectory planning problems for hypersonic gliding vehicle under multiple tasks. Different from the former achievements, this paper takes into account the practical tasks involved in the reentry phase, including penetration of interceptors, evasion of the no-fly zones, and the arrival of the waypoints. Firstly, the constraints during the reentry phase are analyzed in detail, and the original trajectory planning problem is formulated. Secondly, the hp-adaptive pseudospectral discretization method is proposed to effectively reduce the discretization error. Relevant variables are introduced to relax and transform severe intractable constraints of multiple nonconvex forms, thus enhancing the robustness of the planning process. Thirdly, the improved sequential convex programming with decision variables algorithm is proposed to ensure the converged trajectory satisfies complicated tasks. The theoretical analysis is also presented to demonstrate that the converged trajectory is the approximate stationary solution of the discrete form of the original problem. Finally, the effectiveness of the proposed algorithms is validated through numerical simulation.
AB - This paper studies the reentry trajectory planning problems for hypersonic gliding vehicle under multiple tasks. Different from the former achievements, this paper takes into account the practical tasks involved in the reentry phase, including penetration of interceptors, evasion of the no-fly zones, and the arrival of the waypoints. Firstly, the constraints during the reentry phase are analyzed in detail, and the original trajectory planning problem is formulated. Secondly, the hp-adaptive pseudospectral discretization method is proposed to effectively reduce the discretization error. Relevant variables are introduced to relax and transform severe intractable constraints of multiple nonconvex forms, thus enhancing the robustness of the planning process. Thirdly, the improved sequential convex programming with decision variables algorithm is proposed to ensure the converged trajectory satisfies complicated tasks. The theoretical analysis is also presented to demonstrate that the converged trajectory is the approximate stationary solution of the discrete form of the original problem. Finally, the effectiveness of the proposed algorithms is validated through numerical simulation.
KW - Multitask
KW - Reentry trajectory planning
KW - Sequential convex programming
UR - https://www.scopus.com/pages/publications/85205596693
U2 - 10.1016/j.ast.2024.109636
DO - 10.1016/j.ast.2024.109636
M3 - 文章
AN - SCOPUS:85205596693
SN - 1270-9638
VL - 155
JO - Aerospace Science and Technology
JF - Aerospace Science and Technology
M1 - 109636
ER -