Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles
<|reference_start|>Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles: 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.<|reference_end|>
@article{jung2024model,
    title={Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch
  Vehicles},
    author={Ki-Wook Jung, Sang-Don Lee, Cheol-Goo Jung and Chang-Hun Lee},
    journal={arXiv preprint arXiv:2405.01264},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01264},
    primaryClass={eess.SY cs.SY}
}