Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using Multiple Sliding Surfaces
<|reference_start|>Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using Multiple Sliding Surfaces: In any spacecraft landing mission, precision soft landing in a fuel-efficient way while also avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. In order to mitigate this problem, in this paper, a novel near fuel-optimal guidance law is developed to avoid terrain and land precisely and softly at the desired landing site, under atmospheric perturbations and thrust deviations and constraints. Expanding the OTALG formulation by using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves in terms of precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode, and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption and accuracy of precision soft landing. Comparative studies against existing relevant literature validates a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.<|reference_end|>
@article{basar2024robust,
    title={Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using
  Multiple Sliding Surfaces},
    author={Sheikh Zeeshan Basar and Satadal Ghosh},
    journal={arXiv preprint arXiv:2403.12584},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12584},
    primaryClass={eess.SY cs.SY}
}