Auto-tuned Primal-dual Successive Convexification for Hypersonic Reentry Guidance

TL;DR

This paper introduces Auto-SCvx, an auto-tuned primal-dual successive convexification algorithm that reliably finds feasible trajectories for hypersonic reentry, eliminating the need for manual hyperparameter tuning and demonstrating improved performance.

Contribution

The paper presents a novel auto-tuning convexification method for hypersonic reentry guidance that adapts penalty hyperparameters automatically during optimization.

Findings

Auto-SCvx achieves reliable, feasible solutions across various scenarios.

The method outperforms or matches existing techniques in efficiency and accuracy.

No manual tuning of penalty weights is required, simplifying the process.

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.