2024/11/13 by Skye Mceowen, Mceowen, Skye, Daniel Calderone +11 · 2 citations
Engineering · #65K10 #90C25 (primary) #90C46 (secondary) #FOS: Mathematics #Guidance and Control Systems #Optimization and Control (math.OC) #Space Satellite Systems and Control #Spacecraft Dynamics and Control
paper · pdf · doi:10.48550/arxiv.2411.08361
openalex publication_date 2024/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.