Real-Time Model Predictive Control Algorithms for Autonomous Spacecraft Guidance
Abstract
This report studies and compares four families of Model Predictive Control (MPC) algorithms for autonomous spacecraft rendezvous guidance: Linear MPC, Tube MPC, Fast/Embedded MPC, and Successive Convexification (SCvx). Using the Clohessy-Wiltshire-Hill (CWH) relative-motion model, we show that the marginal stability of the underlying dynamics causes the condition number of the condensed Hessian to grow sharply with the prediction horizon, which explains why gradient-based solvers such as Fast MPC diverge under long-horizon stress tests while exact linear-algebra solvers (ADMM with Cholesky factorisation) remain unaffected. Tube MPC is validated across five standard rendezvous manoeuvres (Translation, R-bar, V-bar, Natural Motion Circumnavigation, and Corkscrew) using a single fixed controller configuration, and a two-sided, provably correct bound is derived to bracket the worst-case tracking error directly from the cost weight matrices. The framework is further extended to track an arbitrary, non-closed-form reference trajectory through online-recomputed linearisation, with the safety guarantee holding throughout. Finally, to assess feasibility for real flight software, the core numerical routines (Cholesky factorisation and the Riccati equation solver) are re-implemented from first principles, without external libraries, and validated against standard scientific computing tools to machine precision. Taken together, these results support Tube MPC as a robust and computationally realistic controller for onboard, real-time spacecraft rendezvous guidance.
