Convex Optimization-Based Model Predictive Control for the Guidance of Active Debris Removal Transfers

TL;DR

This paper introduces a convex optimization-based model predictive control method for guiding active debris removal missions, eliminating the need for successive convexification and achieving high accuracy with near-linear orbital elements.

Contribution

The work develops a novel convex MPC approach using near-linear orbital elements and a split-Edelbaum method to improve guidance accuracy without iterative convexification.

Findings

Achieves accurate guidance solutions comparable to high-fidelity models.

Eliminates the need for successive convexification in nonconvex dynamics.

Provides polynomial-time solutions for debris removal guidance.

Abstract

Active debris removal (ADR) missions have garnered significant interest as means of mitigating collision risks in space. This work proposes a convex optimization-based model predictive control (MPC) approach to provide guidance for such missions. While convex optimization can obtain optimal solutions in polynomial time, it relies on the successive convexification of nonconvex dynamics, leading to inaccuracies. Here, the need for successive convexification is eliminated by using near-linear Generalized Equinoctial Orbital Elements (GEqOE) and by updating the reference trajectory through a new split-Edelbaum approach. The solution accuracy is then measured relative to a high-fidelity dynamics model, showing that the MPC-convex method can generate accurate solutions without iterations.