The problem of achieving pinpoint landing accuracy in future space missionsto extra-terrestrial bodies such as the Moon or Mars presents many challenges,including the requirements of higher accuracy and more flexibility. These newchallenges may require the development of novel and more advanced guidancealgorithms. Conventional guidance schemes, which generally require a combinationof off-line trajectory generation and real-time, trajectory tracking algorithms,have worked well in the past but may not satisfy the more stringent anddifficult landing requirements imposed by future mission architectures to bringlanders very near to specified locations. In this paper, a novel non-linear guidancealgorithm for planetary landing is proposed and analyzed. Based onHigher-Order Sliding Control (HOSC) theory, the Multiple Sliding SurfaceGuidance (MSSG) algorithms has been specifically designed to take advantageof the ability of the system to reach the sliding surface in a finite time. Thehigh control activity seen in typical sliding controllers is avoided in this formulation,resulting in a guidance law that is both globally stable and robustagainst unknown, but bounded perturbations. The proposed MSSG does not requireany off-line trajectory generation and therefore it is flexible enough totarget a large variety of point on the planet's surface without the need for calculationof multiple reference trajectories. However, after initial analysis, it hasbeen seen that the performance of MSSG is very sensitive to the choice inguidance gains. MSSG generated trajectories have been compared to an optimalsolution to begin an investigation of the relationship between theoptimality and performance of MSSG and the selection of the guidance parameters.A full study has been performed to investigate and tune the parametersof MSSG utilizing reinforcement learning in order to truly optimize the performanceof the MSSG algorithm. Results show that the MSSG algorithm can indeedgenerate trajectories that come very close to the optimal solution in termsof fuel usage. A full comparison of the trajectories is included, as well as a furtherstudy examining the capability of the MSSG algorithm under perturbedconditions using the optimized set of parameters.
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