# Advances in Trajectory Optimization for Space Vehicle Control arXiv: https://arxiv.org/abs/2108.02335 ยท HTML: https://mlsanity.com/paper/2108.02335/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 43 of its 477 references that are in the map. - First posted on arXiv 5 Aug 2021. ## Builds on (most cited first, 30 of 43) - [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021) - [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017) - [JuMP: A Modeling Language for Mathematical Optimization](https://mlsanity.com/paper/1508.01982/) (2015) - [Funnel Libraries for Real-Time Robust Feedback Motion Planning](https://mlsanity.com/paper/1601.04037/) (2016) - [Real-time optimal control via Deep Neural Networks: study on landing problems](https://mlsanity.com/paper/1610.08668/) (2016) - [A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics](https://mlsanity.com/paper/1908.05209/) (2019) - [A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control](https://mlsanity.com/paper/1812.02948/) (2018) - [Real-Time Optimal Control for Irregular Asteroid Landings Using Deep Neural Networks](https://mlsanity.com/paper/1901.02210/) (2019) - [GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming](https://mlsanity.com/paper/1903.00155/) (2019) - [Successive Convexification: A Superlinearly Convergent Algorithm for Non-convex Optimal Control Problems](https://mlsanity.com/paper/1804.06539/) (2018) - [Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.02182/) (2020) - [Fast Trajectory Optimization via Successive Convexification for Spacecraft Rendezvous with Integer Constraints](https://mlsanity.com/paper/1906.04857/) (2019) - [Convex Optimization for Trajectory Generation](https://mlsanity.com/paper/2106.09125/) (2021) - [Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time](https://mlsanity.com/paper/1802.03827/) (2018) - [Interior Point Differential Dynamic Programming](https://mlsanity.com/paper/2004.12710/) (2020) - [Convex Optimization of Launch Vehicle Ascent Trajectory with Heat-Flux and Splash-Down Constraints](https://mlsanity.com/paper/2008.13239/) (2020) - [Fast Homotopy for Spacecraft Rendezvous Trajectory Optimization with Discrete Logic](https://mlsanity.com/paper/2107.07001/) (2021) - [Adaptive Guidance with Reinforcement Meta-Learning](https://mlsanity.com/paper/1901.04473/) (2019) - [Dual Quaternion Based Powered Descent Guidance with State-Triggered Constraints](https://mlsanity.com/paper/1904.09248/) (2019) - [How Many Impulses Redux](https://mlsanity.com/paper/1906.01839/) (2019) - [Data-driven Stabilization of SISO Feedback Linearizable Systems](https://mlsanity.com/paper/2003.14240/) (2020) - [Constrained Spacecraft Relative Motion Planning Exploiting Periodic Natural Motion Trajectories and Invariance](https://mlsanity.com/paper/1703.06313/) (2017) - [Solar-Sail Deep Space Trajectory Optimization Using Successive Convex Programming](https://mlsanity.com/paper/1901.02145/) (2019) - [Successive Convexification for 6-DoF Powered Descent Guidance with Compound State-Triggered Constraints](https://mlsanity.com/paper/1901.02181/) (2019) - [Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control](https://mlsanity.com/paper/2009.05038/) (2020) - [Low-Thrust Transfer Between Circular Orbits Using Natural Precession](https://mlsanity.com/paper/1503.00644/) (2015) - [Discrete-time optimal attitude control of spacecraft with momentum and control constraints](https://mlsanity.com/paper/1509.04521/) (2015) - [Pulse-Width Predictive Control for LTV Systems with Application to Spacecraft Rendezvous](https://mlsanity.com/paper/1511.00869/) (2015) - [Fast, Safe, and Propellant-Efficient Spacecraft Planning under Clohessy-Wiltshire-Hill Dynamics](https://mlsanity.com/paper/1601.00042/) (2016) - [A discrete-time Pontryagin maximum principle on matrix Lie groups](https://mlsanity.com/paper/1612.08022/) (2016) ## Built on by (most cited first, 5 of 5) - [Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling](https://mlsanity.com/paper/2410.23916/) (2024) - [Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers](https://mlsanity.com/paper/2410.11723/) (2024) - [Deep Learning Warm Starts for Trajectory Optimization on the International Space Station](https://mlsanity.com/paper/2505.05588/) (2025) - [EclipseNETs: Learning Irregular Small Celestial Body Silhouettes](https://mlsanity.com/paper/2504.04455/) (2025) - [Saturation-Aware Robust Trajectory Optimization for Reusable Launch Vehicles via Differentiable Physics](https://mlsanity.com/paper/2607.09736/) (2026) ## Abstract (arXiv) Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.