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一种基于可钻性在线辨识的月面钻进控制方法研究 被引量:1

Control Method of Lunar Drilling Based on Online Identification of Drilling Ability
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摘要 钻取采样作为一种获取深层月壤的有效方式被应用于地外天体采样任务。不同于地面钻探,无人月面钻取采样面临诸多技术难点,例如遥操作信号延迟、探测器传感资源有限、缺乏采样点地质信息以及月壤力学特性复杂等。为保证采样任务高效可靠地执行,采样装置需充分利用有限的探测器硬件资源,依据钻进工况实时调整钻进工艺参数,对未知的钻进环境具有适应能力。提出一种基于可钻性在线辨识的月面钻进控制方法。利用可钻性指标综合评价当前对象的钻进难易程度,采用模式识别方法辨识钻进对象的可钻性等级并实时匹配最优的钻进工艺参数,从而实现钻进过程的智能控制。为验证所提出控制方法的有效性,开展了模拟月壤月岩交替布置的钻进试验研究。试验结果表明:该方法能够有效控制钻进负载。 Drilling and coring,as an effective method of acquiring deep lunar regolith,has been widely applied in extraterrestrial sampling missions.Different from drilling on Earth,unmanned lunar drilling & coring may meet several technical problems,such as time delays in remote control,limited sensor resources,lack of geological information on sampling site,complicated mechanical properties of lunar regolith and so on.To realize high efficient drilling process with high reliability and have adaptability on unknown drilling environment,sampling device should adjust drilling parameters online depending on the real-time drilling conditions by limited hardware resources on the probe.This paper proposed a control method of lunar drilling based on online identification of drilling ability.The intelligent drilling control method has been realized by using drilling ability index to describe the drilling difficulty level,adopting pattern recognition method to identify the drilling ability levels and matching the optimized drilling parameters online.In order to verify the proposed control method,the drilling experiment in a multi-layered simulation mixed with granular soil and hard rocks has been conducted.Experimental results showed that drilling load under this control method could be controlled effectively.
出处 《深空探测学报》 2015年第4期325-332,共8页 Journal Of Deep Space Exploration
基金 国家自然科学基金资助项目(61403106)
关键词 月球探测 无人钻取采样 钻进控制 可钻性 在线辨识 lunar exploration unmanned drilling & coring drilling control drilling ability online identification
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  • 1彭宁云,文习山,王一,陈江波,柴旭峥.基于线性分类器的充油变压器潜伏性故障诊断方法[J].中国电机工程学报,2004,24(6):147-151. 被引量:35
  • 2莫娟,王雪,董明,严璋.基于粗糙集理论的电力变压器故障诊断方法[J].中国电机工程学报,2004,24(7):162-167. 被引量:85
  • 3吕干云,程浩忠,翟海保,董立新.基于改进灰色关联分析的变压器故障识别[J].中国电机工程学报,2004,24(10):121-126. 被引量:42
  • 4Huang Y C.Condition assessment of power transformers using genetic-based neural networks[J].IEE Proc.-Sci.Meas.Technol, 2003,150(1):19-24.
  • 5Krishnapuram R,Keller J M.A possibilistic approach to clustering[J]. IEEE Transactionson Fuzzy Systems,1993,1(5):98-110.
  • 6Wu Zhongdong,Xie Weixin,Yu Jianping et al.Fuzzy C-means clustering algorithm based on kernel method[C].Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications,Xi'an,China,2003.
  • 7Xie X L,Beni G.A validity measure for fuzzy clustering[J].IEEE Transcation on Pattern Analysis and Machine Intelligence,1991,13(8):841-847.
  • 8Sandel B R, Broadfoot A L, Curtis C C, et al. The extreme ultraviolet imager investigation for the image mission. Space Sci Rev, 2000, 91: 197-242.
  • 9Bell III J F, Squyres S W, Herkenhoff K E, et al. Mars exploration rover athena panoramic camera (pancam) investigation. J Geophys Res, 2003, 108:8063.
  • 10Klingelhofer G, Bruckner J, D'uston C, et al. The rosetta alpha particle X-ray spectrometer (APXS). Space Sci Rev, 2007, 128:383-396.

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