A manually retargeted automatic landing system for lunar module /LM/.

Klumpp, Allan R.

doi:10.2514/3.29204

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…c and are known constants and computed using (1). The initial state of the lander is defined by the perilune of an entry orbit with known perilune and apolune altitudes, and , and by the orbital velocity at this point and the initial mass at :…”

Section: A Problem Statementmentioning

confidence: 99%

“…The pre-quartic part of the phase will be investigated in the next subsections. The quartic formulation allows us to satisfy five constraints on the reference trajectory [1]. As is known, the main part of the descent trajectory consists of three phases: braking, approach and terminal descent.…”

Section: Modification and Extension Of Braking-phase Targetingmentioning

confidence: 99%

“…The terminology used is based on the general definitions, conventions and the flowchart diagrams employed in Apollo missions [1]. The lunar descent and landing was comprised of ballistic, ullage, trim, braking, approach and terminal descent phases.…”

Section: Introductionmentioning

confidence: 99%

“…The guidance algorithm was executed onboard and at each cycle it provided a re-designated landing site, commanded acceleration, commanded unit thrust and window vectors, updated target referenced time, and updated transformation matrices from the platform system to guidance and body systems. The descent and landing trajectory is nearly planar and significant part of the trajectory is approximated by two quartic polynomials [1]. The approach phase (AP) quartic is defined in a closed form, while the determination of the braking phase (BP) quartic requires iterations in order to satisfy the terminal constraints as the first part of the braking phase has no solution and the state at initial point of the braking phase is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…As previously mentioned, the part of the braking phase from the Dilmurat Azimov is with the University of Hawaii at Manoa, Holmes 202A, Honolulu, HI, 96822, USA. entry orbit to the initial point of the throttle recovery phase had no closed form solution, hence the Apollo ground-based targeting programs used numerical iterations to compute a viable trajectory on the braking phase and to compute the target states for the guidance program [1]. This means that the guidance target states cannot be modified during the actual descent, thereby eliminating the possibility of a practical realtime targeting capability.…”

Section: Introductionmentioning

confidence: 99%

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Extremal Solutions for Powered Lunar-Descent Guidance and Targeting

2016

6th International Conference on Advances in Engineering Sciences and Applied Mathematics (ICAESAM-2016) Dec. 21-22, 2016 Kuala

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Abstract-A new closed-form solution for the constant thrust arc of the braking phase of a lunar descent trajectory is presented as an enhancement of Apollo ground-based targeting, thereby enabling an Apollo-class explicit real-time on-board targeting procedures. It is shown that integrated real-time guidance and targeting can be formulated entirely by closed-form extremal solutions. The trajectory and guidance performances, simulated using these solutions, have been shown to compare favorably with the reconstructed Apollo 12 descent trajectory.

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Section: A Problem Statementmentioning

confidence: 99%

Section: Modification and Extension Of Braking-phase Targetingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Extremal Solutions for Powered Lunar-Descent Guidance and Targeting

2016

6th International Conference on Advances in Engineering Sciences and Applied Mathematics (ICAESAM-2016) Dec. 21-22, 2016 Kuala

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Terminal Multiple Surface Sliding Guidance for Planetary Landing: Development, Tuning and Optimization via Reinforcement Learning

et al. 2015

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The problem of achieving pinpoint landing accuracy in future space missions to planetary bodies such as the Moon or Mars presents many challenges, including the requirements of higher accuracy and degree of flexibility. These new challenges may require the development of a new class of guidance algorithms. In this paper, a nonlinear guidance algorithm for planetary landing is proposed and analyzed. Based on Higher-Order Sliding Control (HOSC) theory, the Multiple Sliding Surface Guidance (MSSG) algorithm has been specifically designed to take advantage of the ability of the system to reach multiple sliding surfaces in a finite time. As a result, a guidance law that is both globally stable and robust against unknown, but bounded perturbations is devised. The proposed MSSG does not require any offline trajectory generation, but the acceleration command is instead generated directly as function of the current and final (target) state. However, after initial analysis, it has been noted that the performance of MSSG critically depends on the choice in J of Astronaut Sci guidance gains. MSSG-guided trajectories have been compared to an open-loop fuelefficient solution to investigate the relationship between the MSSG fuel performance and the selection of the guidance parameters. A full study has been executed to investigate and tune the parameters of MSSG utilizing reinforcement learning in order to truly optimize the performance of the MSSG algorithm in powered descent scenarios. Results show that the MSSG algorithm can indeed generate closed-loop trajectories that come very close to the optimal solution in terms of fuel usage. A full comparison of the trajectories is included, as well as a further Monte Carlo analysis examining the guidance errors of the MSSG algorithm under perturbed conditions using the optimized set of parameters.

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Integrated Targeting and Guidance for Powered Planetary Descent

Azimov

2018

J of Astronaut Sci

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A manually retargeted automatic landing system for lunar module /LM/.

Cited by 18 publications

References 5 publications

Extremal Solutions for Powered Lunar-Descent Guidance and Targeting

Extremal Solutions for Powered Lunar-Descent Guidance and Targeting

Terminal Multiple Surface Sliding Guidance for Planetary Landing: Development, Tuning and Optimization via Reinforcement Learning

Integrated Targeting and Guidance for Powered Planetary Descent

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