Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles

TL;DR

This paper presents a model predictive guidance strategy for fuel-efficient landing of reusable launch vehicles, combining trajectory planning and real-time control with enhanced model fidelity and aerodynamic modifications, validated through extensive simulations.

Contribution

It introduces a dual optimal control framework using sequential convex programming for RLV landing guidance, improving computational efficiency and model accuracy.

Findings

Achieves near real-time guidance with reduced computational load.

Enhances model fidelity for stable numerical solutions.

Validates effectiveness through extensive 6-DOF simulations.

Abstract

This paper introduces a landing guidance strategy for reusable launch vehicles (RLVs) using a model predictive approach based on sequential convex programming (SCP). The proposed approach devises two distinct optimal control problems (OCPs): planning a fuel-optimal landing trajectory that accommodates practical path constraints specific to RLVs, and determining real-time optimal tracking commands. This dual optimization strategy allows for reduced computational load through adjustable prediction horizon lengths in the tracking task, achieving near closed-loop performance. Enhancements in model fidelity for the tracking task are achieved through an alternative rotational dynamics representation, enabling a more stable numerical solution of the OCP and accounting for vehicle transient dynamics. Furthermore, modifications of aerodynamic force in both planning and tracking phases are proposed, tailored for thrust-vector-controlled RLVs, to reduce the fidelity gap without adding computational complexity. Extensive 6-DOF simulation experiments validate the effectiveness and improved guidance performance of the proposed algorithm.