@article{WANG2025, 
author = {Yangjie WANG and Teng LONG and Junzhi LI and Guangtong XU and Jingliang SUN},
title = {Re-entry trajectory planning for hypersonic morphing vehicles using penalty sequence convex programming},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {5},
pages = {1747-1759},
keywords = {hypersonic morphing vehicle, reentry trajectory planning, logarithmic convexification, virtual control, adaptive trust region},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0283},
doi = {10.13700/j.bh.1001-5965.2023.0283},
abstract = {To realize continuous leapfrog upgrades of the hypersonic vehicle from single-point optimal fixed configuration to full envelope optimal of morphing configuration, a quasi-wave rider profile and composite deformation scheme morphing wingspan and sweep are designed. On this basis, to reduce the computational burdens of reentry trajectory planning, the adaptive trust-region-based penalty sequence convex programming method is proposed. To increase the approximate accuracy, the path restrictions are communicated using the logarithmic convexification technique. A virtual control is introduced to replace the dynamic equation constraints. Using the penalty function method, modify the second-order cone constraint and incorporate it into the objective function to direct the iterative results in order to approximate the feasible domain. An adaptive trust region updating strategy is designed to accelerate the convergence of the sequence convex optimization algorithm. As demonstrated by the simulation results, the hypersonic morphing vehicle's range extension is 16.63% when compared to the fixed configuration, and the ATP-SCP computing time is 89.24% less than when compared to the HP pseudospectral method.}
}