# Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction

arXiv: https://arxiv.org/abs/2311.05135 · HTML: https://mlsanity.com/paper/2311.05135/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Online Mixed-Integer Optimization in Milliseconds](https://mlsanity.com/paper/1907.02206/) (2019)
- [The Voice of Optimization](https://mlsanity.com/paper/1812.09991/) (2018)
- [GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming](https://mlsanity.com/paper/1903.00155/) (2019)
- [Convex Optimization for Trajectory Generation](https://mlsanity.com/paper/2106.09125/) (2021)
- [Guided Policy Search using Sequential Convex Programming for Initialization of Trajectory Optimization Algorithms](https://mlsanity.com/paper/2110.06975/) (2021)
- [Transformer-based Atmospheric Density Forecasting](https://mlsanity.com/paper/2310.16912/) (2023)
- [Dual Quaternion Based Powered Descent Guidance with State-Triggered Constraints](https://mlsanity.com/paper/1904.09248/) (2019)
- [Real-Time Sequential Conic Optimization for Multi-Phase Rocket Landing Guidance](https://mlsanity.com/paper/2212.00375/) (2022)

## Built on by (most cited first, 5 of 5)

- [Diffusion Policies for Generative Modeling of Spacecraft Trajectories](https://mlsanity.com/paper/2501.00915/) (2025)
- [Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control](https://mlsanity.com/paper/2512.16824/) (2025)
- [Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints](https://mlsanity.com/paper/2410.04261/) (2024)
- [Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance](https://mlsanity.com/paper/2501.00930/) (2025)
- [Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition](https://mlsanity.com/paper/2610.05642/) (2026)

## Abstract (arXiv)

In this work, we present Transformer-based Powered Descent Guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guidance problem. T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters and the globally optimal solution for the powered descent guidance problem. The solution is encoded as the set of tight constraints corresponding to the constrained minimum-cost trajectory and the optimal final time of landing. By leveraging the attention mechanism of transformer neural networks, large sequences of time series data can be accurately predicted when given only the spacecraft state and landing site parameters. When applied to the real problem of Mars powered descent guidance, T-PDG reduces the time for computing the 3 degree of freedom fuel-optimal trajectory, when compared to lossless convexification, from an order of 1-8 seconds to less than 500 milliseconds. A safe and optimal solution is guaranteed by including a feasibility check in T-PDG before returning the final trajectory.