All work

AIAA Regional Student Conferences · 2026

Optimal Powered-Descent Guidance

I co-authored a Rust-based study comparing zero-order-hold and Chebyshev-Gauss-Lobatto formulations for losslessly convexified powered descent.

Expanded technical stack

  • Rust
  • Lossless convexification
  • Zero-order hold
  • Chebyshev-Gauss-Lobatto
  • Pseudospectral methods
  • Trajectory optimization
  • Numerical methods
  • 3-DOF guidance
  • Benchmarking / numerical analysis
Technical diagram illustrating an optimal powered-descent trajectory and glideslope constraint
Foundational powered-descent geometry, adapted from Blackmore et al.

Overview

Lossless convexification relaxes the nonconvex minimum-thrust and pointing constraints of soft landing while preserving the optimum. Our team needed to choose a numerical formulation suitable for onboard path planning.

My contribution

I co-authored the paper and implemented work for the three-degree-of-freedom Rust guidance study, comparing time-discretized zero-order hold with a Chebyshev pseudospectral formulation.

Challenge

We needed to choose between a sparse, locally coupled ZOH discretization and a smaller but globally coupled Chebyshev formulation for real-time, fuel-aware VTVL descent planning.

Technical approach

We benchmarked both solvers across four 50-meter descent profiles and four resolution levels, comparing solve time, position and thrust RMSE, and maximum thrust-rate behavior against a high-resolution ZOH reference.

Result / outcome

Our study found that Chebyshev-CGL produced more accurate coarse-resolution trajectories and substantially smoother controls. We selected it as the team's primary onboard approach while identifying independent ground-truth validation as future work.