Multi-constrained suboptimal powered descent guidance for lunar pinpoint soft landing

A multi-constrained suboptimal guidance method based on an improved zero-effort-miss/zero-effort-velocity (ZEM/ZEV) algorithm and the recently developed model predictive static programming (MPSP) is presented in this paper for lunar pinpoint soft landing. Firstly, the ZEM/ZEV algorithm is improved s...

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Vydáno v:Aerospace science and technology Ročník 48; s. 203 - 213
Hlavní autoři: Zhang, Bo, Tang, Shuo, Pan, Binfeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Masson SAS 01.01.2016
Témata:
Algorithms
Computer simulation
Descent
Guidance (motion)
Improved zero-effort-miss/zero-effort-velocity algorithm
Mathematical models
Model predictive static programming
Multiple constraints
Pinpoint soft landing
Soft landing
Suboptimal guidance
Thrust
Trajectories
Suboptimal guidance
Pinpoint soft landing
Multiple constraints
Improved zero-effort-miss/zero-effort-velocity algorithm
Model predictive static programming
ISSN:1270-9638, 1626-3219
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Shrnutí:A multi-constrained suboptimal guidance method based on an improved zero-effort-miss/zero-effort-velocity (ZEM/ZEV) algorithm and the recently developed model predictive static programming (MPSP) is presented in this paper for lunar pinpoint soft landing. Firstly, the ZEM/ZEV algorithm is improved so that the trajectories generated by the algorithm are always above the surface of the Moon without thrust magnitude and look-angle constraints violated. A concept of virtual control is introduced for the continuity of the guidance commands and the enforcement of the thrust vector constraint at the terminal point. Taking the trajectory generated by the improved ZEM/ZEV algorithm as the initial guess history of the MPSP method, and the virtual control history as its control history, we develop a multi-constrained fuel suboptimal powered descent guidance law with the help of the high computational efficiency of the MPSP technique. Extensive simulations are conducted to verify the design features of the algorithm. The testing results demonstrate that the proposed algorithm is accurate and robust, and has a good capability of retargeting.
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ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2015.11.018