Advances in Trajectory Optimization for Space Vehicle Control

TL;DR

This survey reviews recent advances in optimization techniques like convexification and model predictive control, highlighting their growing application in space vehicle guidance and control over the past decade.

Contribution

It provides a comprehensive overview of recent optimization methods and their successful application in various space vehicle control scenarios, emphasizing progress in the last ten years.

Findings

Rise in applications using convex optimization techniques.

Successful deployment in planetary landing and orbit transfer.

Identification of promising future research directions.

Abstract

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.