Auto-Tuned Primal-Dual Successive Convexification for Hypersonic Reentry Guidance

@inbook{doi:10.2514/6.2025-1317,
author = {Skye Mceowen and Daniel J. Calderone and Aman Tiwary and Jason S. Zhou and Taewan Kim and Purnanand Elango and Behcet Acikmese},
title = {Auto-Tuned Primal-Dual Successive Convexification for Hypersonic Reentry Guidance},
booktitle = {AIAA SCITECH 2025 Forum},
chapter = {},
pages = {},
doi = {10.2514/6.2025-1317},
URL = {https://arc.aiaa.org/doi/abs/10.2514/6.2025-1317},
eprint = {https://arc.aiaa.org/doi/pdf/10.2514/6.2025-1317},
    abstract = { This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission parameter space. In Auto-SCvx, we solve a sequence of convex subproblems until convergence to a solution of the original nonconvex problem. This method iteratively optimizes dual variables in closed-form in order to update the penalty hyperparameters used in the primal variable updates. A benefit of this method is that it is auto-tuning, and requires no hand-tuning by the user with respect to the constraint penalty weights. Several example hypersonic reentry problems are posed and solved using this method, and comparative studies are conducted against current methods. In these numerical studies, our algorithm demonstrates equal and often improved performance while not requiring hand-tuning of penalty hyperparameters. }
}