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All Journal Indonesian Journal of Aerospace
Zani, Novanna Rahma
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Aerospace Engineering Astronomy

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Journal : Indonesian Journal of Aerospace

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POWERED LANDING GUIDANCE ALGORITHMS USING REINFORCEMENT LEARNING METHODS FOR LUNAR LANDER CASE Nugroho, Larasmoyo; Zani, Novanna Rahma; Qomariyah, Nurul; Akmeliawati, Rini; Andiarti, Rika; Wijaya, Sastra Kusuma
Indonesian Journal of Aerospace Vol. 19 No. 1 (2021)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.jtd.2021.v19.a3573

Abstract

Any future planetary landing missions, just as demonstrated by Perseverance in 2021 Mars landing mission require advanced guidance, navigation, and control algorithms for the powered landing phase of the spacecraft to touch down a designated target with pinpoint accuracy (circular error precision < 5 m radius). This requires a landing system capable to estimate the craft’s states and map them to certain thrust commands for each craft’s engine. Reinforcement learning theory is used as an approach to manage the mapping guidance algorithm and translate it to engine thrust control commands. This work compares several reinforcement learning based approaches for a powered landing problem of a spacecraft in a two-dimensional (2-D) environment, and identify the advantages/disadvantages of them. Five methods in reinforcement learning, namely Q-Learning, and its extension such as DQN, DDQN, and policy optimization-based such as DDPG and PPO are utilized and benchmarked in terms of rewards and training time needed to land the Lunar Lander. It is found that Q-Learning method produced the highest efficiency. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system’s performance in a 2-D simulation environment and demonstrate robustness to noise and system parameter uncertainty.
Co-Authors Andiarti, Rika Larasmoyo Nugroho Nurul Qomariyah Rini Akmeliawati Sastra Kusuma Wijaya