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All Journal Indonesian Green Technology Journal Indonesian Journal of Aerospace
Zani, Novanna Rahma
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Earth & Planetary Sciences Engineering Other

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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.
Design and Development of Soil Nutrients Level Detection System based on Soil Color and pH for Crop Recommendations using Fuzzy Algorithms Zani, Novanna Rahma; Alasiry, Ali Husein; Wijayanto, Ardik
Indonesian Green Technology Journal Vol. 11 No. 1 (2022): Indonesian Green Technology Journal
Publisher : Sekolah Pascasarjana, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.igtj.2022.011.01.05

Abstract

In recent years, modern farmers usually taking a soil sample to the laboratory or using a soil test kit to know soil macronutrients, i.e., nitrogen (N), phosphorus (P), and potassium (K), and pH to determine what kind of crop plant is suitable for their agriculture land. However, these manual methods are costly and time consumed. The characteristic of soil samples also possibly changing by time or contact during transport. This paper presents the design and development of a portable integrated soil macronutrient level and pH detection system that can analyze soil samples quickly. To give crop recommendations, IoT components and cloud-based fuzzy inference systems are used. The fuzzy algorithm decides the crop recommendation from the soil pH and level content of N, P, and K. The user can receive the crop recommendation via the android application. Data is sent from the portable system to the cloud system and vice versa using the internet network with HTTP request protocol. The accuracy test results of system plant decision on agricultural land were compared with the fuzzy logic method have a quite uniform crop output with a small error rate of 1,66%. Keywords: Color sensor, Soil pH sensor, Soil NPK, Fuzzy Logic, Plant recommendation
Co-Authors Ali Husein Alasiry Andiarti, Rika Ardik Wijayanto Larasmoyo Nugroho Nurul Qomariyah Rini Akmeliawati Sastra Kusuma Wijaya