基于anfiss的探月钻孔取芯装置控制策略

Chongbin Chen, Q. Quan, Shengyuan Jiang, Z. Deng
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引用次数: 1

摘要

中国月球探测的第二步已经完成,嫦娥三号探测器成功着陆月球,包括着陆器和月球车。该项目的第三步是通过钻孔取芯装置实现月球风化层的自动采样。在月球上,沿纵向可能会随机遇到月球风化层和月球岩石。由于采样装置的钻井环境具有不确定性,因此在钻井过程中,自动化控制变得至关重要。提出了一种基于自适应神经模糊推理系统(ANFIS)的控制策略,以解决月球表面下复杂的钻井介质问题。该网络通过典型的月球风化层模拟和月球岩石模拟进行训练,具有较高的识别率。采用模拟月球风化层和模拟月球岩石构建多层钻井介质,进行钻井实验试验。实验表明,基于ANFIS的控制策略能够很好地适应复杂环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANFIS-based control strategy for a drilling and coring device in lunar exploration
The second step for lunar exploration of China has been completed already, the probe “Chang'e-3” successfully landed on the moon, including a lander and a lunar rover. The third step of the project is to achieve automated sampling of lunar regolith through a drilling and coring device. On the moon, lunar regolith and lunar rock may be encountered randomly along the longitudinal direction. Due to the indeterminable drilling environments for the sampling device, the automated control becomes very crucial in the drilling process. This paper proposed an adaptive neuro-fuzzy inference system (ANFIS) based control strategy to tackle the complex drilling media beneath lunar surface. The network is trained through typical lunar regolith simulants and lunar rock simulants, with high identification ratio. A multi-layered drilling medium is built with lunar regolith simulant and lunar rock simulant for drilling experimental test. Experiments indicate that the ANFIS based control strategy can adapt to the complex environment well.
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