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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 工程科學及海洋工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21172
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dc.contributor.advisor郭振華
dc.contributor.authorHong-Ruei Changen
dc.contributor.author張紘瑞zh_TW
dc.date.accessioned2021-06-08T03:28:07Z-
dc.date.copyright2019-10-17
dc.date.issued2019
dc.date.submitted2019-09-27
dc.identifier.citation[1] Sfakiotakis, Michael, David M. Lane, and J. Bruce C. Davies. 'Review of fish swimming modes for aquatic locomotion.' IEEE Journal of oceanic engineering 24.2 (1999): 237-252.
[2] Katz, Yael, et al. 'Inferring the structure and dynamics of interactions in schooling fish.' Proceedings of the National Academy of Sciences 108.46 (2011): 18720-18725.
[3] Weihs, D. 'Hydromechanics of fish schooling.' Nature 241.5387 (1973): 290.
[4] T. J. Pitcher, B. L. Partridge, and C. S. Wardle, 'Blind Fish Can School,' Science, vol. 194, pp. 963-965, 1976.
[5] Ren, Zheng, and Kamran Mohseni. 'A model of the lateral line of fish for vortex sensing.' Bioinspiration & biomimetics 7.3 (2012): 036016.
[6] Salumäe, Taavi, and Maarja Kruusmaa. 'Flow-relative control of an underwater robot.' Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 469.2153 (2013): 20120671.
[7] Dahleh, Mohammed, Munther A. Dahleh, and George Verghese. 'Lectures on dynamic systems and control.' A+ A 4.100 (2004): 1-100.
[8] Krener, Arthur J., and Kayo Ide. 'Measures of unobservability.' Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference. IEEE, 2009.
[9] Singh, Abhay K., and Juergen Hahn. 'Determining optimal sensor locations for state and parameter estimation for stable nonlinear systems.' Industrial & engineering chemistry research44.15 (2005): 5645-5659.
[10] DeVries, Levi, and Derek A. Paley. 'Observability-based optimization for flow sensing and control of an underwater vehicle in a uniform flowfield.' 2013 American Control Conference. IEEE, 2013.
[11] Fernandez, Vicente I., et al. 'Lateral-line-inspired sensor arrays for navigation and object identification.' Marine Technology Society Journal 45.4 (2011): 130-146.
[12] Abdulsadda, Ahmad T., and Xiaobo Tan. 'Underwater tracking of a moving dipole source using an artificial lateral line: algorithm and experimental validation with ionic polymer–metal composite flow sensors.' Smart Materials and Structures 22.4 (2013): 045010.
[13] Dang, Fengying, and Feitian Zhang. 'Distributed Flow Estimation for Autonomous Underwater Robots Using POD-based Model Reduction.' 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018.
[14] Abdulsadda, Ahmad T., and Xiaobo Tan. 'Nonlinear estimation-based dipole source localization for artificial lateral line systems.' Bioinspiration & biomimetics 8.2 (2013): 026005.
[15] Streitlien, K., and M. S. Triantafyllou. 'Force and moment on a Joukowski profile in the presence of point vortices.' AIAA journal33.4 (1995): 603-610.
[16] Mason, Richard James. Fluid locomotion and trajectory planning for shape-changing robots. Diss. California Institute of Technology, 2003.
[17] Romanenko, Evgeniĭ Vasilʹevich. Fish and dolphin swimming. Pensoft Publishers, 2002.
[18] Yen, Wei-Kuo, and Jenhwa Guo. 'Synchronizing the tail motion of a robotic fish to a periodic moving source.' 2013 CACS International Automatic Control Conference (CACS). IEEE, 2013.
[19] Katz, Joseph, and Allen Plotkin. Low-speed aerodynamics. Vol. 13. Cambridge university press, 2001.
[20] Aster, Richard C., Brian Borchers, and Clifford H. Thurber. Parameter estimation and inverse problems. Elsevier, 2018.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21172-
dc.description.abstract本論文利用水下載具側身的壓力計陣列以量測機器魚尾鰭振動所造成的流體壓力變化,來估測前行機器魚的位置。根據勢流理論建立出前導者(機器魚)之尾鰭振動壓力場受尾隨者(水下載具)影響下的壓力場數學模式。首先藉由非線性觀測矩陣計算前導者和尾隨者在不同相對位置下的觀測度,並根據所建立之壓力場模式估測前導者的所在位置,並討論其不確定性。定位方法與其誤差分析之數值模擬結果顯示前導者縱向與橫向的定位誤差大小隨著與尾隨者間的相對位置而改變,且當前導者在尾隨者前方扇形區域內有較小的定位誤差,此結果與觀測度所預測之結果相符;水槽試驗亦獲得類似的結果。自然界的魚群編隊型態亦可見尾隨者在後方的群游樣式。zh_TW
dc.description.abstractThis thesis investigates the estimator design for following a vibrating tail of a robot fish by pressure measurement behind the fish. The pressure field model of the leader (a robotic fish) and the follower (an underwater vehicle) is formulated based on the potential flow theory. Empirical observability gramian is utilized to find the relative position that the leader and follower have better observability. An iterative method based on inversion of a linearized measurement equation is used to estimate the position of the leader. The uncertainty of the estimation is plotted as an uncertainty map to show best tracking positions for the follower. The experimental results show that the leader will have a better estimation in front of the follower. The positional uncertainties are relative-location dependent between the leader and the follower. We conclude that tracking of a robot fish through hydrodynamic pressure variations is possible. The study suggests some better positions for the follower where the estimation of the leader position uncertainties is smaller. The suggested formation pattern generally can be found in a natural fish schooling.en
dc.description.provenanceMade available in DSpace on 2021-06-08T03:28:07Z (GMT). No. of bitstreams: 1
ntu-108-R06525099-1.pdf: 2274439 bytes, checksum: dcf3511511d3f4458900eae078e0c234 (MD5)
Previous issue date: 2019
en
dc.description.tableofcontents誌謝 ii
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURE vii
LIST OF SYMBOLS xii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Literature review 2
1.3 Thesis organization 4
Chapter 2 Mathematical model 6
2.1 Pressure field of the leader 6
2.2 Building the shape model of the follower 8
2.3 Pressure field in the presence of follower 12
2.4 Validation of pressure model 22
2.5 Summary 27
Chapter 3 Observability and estimation of the leader position 29
3.1 Observability 29
3.2 Selection for the placement of pressure sensors 32
3.3 Estimation of the leader position 42
3.4 Error analysis 46
Chapter 4 Tank experiments 56
4.1 Leader and follower 56
4.1.1 Pressure sensors 57
4.2 Laser scanner 58
4.3 Experimental setup 59
Chapter 5 Experimental results and discussion 63
5.1 Filtering out the pressure generated by the follower 63
5.2 Case 1: Leader on the starboard bow of the follower 66
5.3 Case 2: Leader at ahead of the follower 70
5.4 Case 3: Leader at abeam of the follower 75
5.5 Discussion 79
Chapter 6 Conclusions 80
6.1 Conclusions 80
6.2 Suggestions for future research 81
REFERENCES 83
Appendix 85
dc.language.isozh-TW
dc.title利用尾隨載具的側線壓力陣列於被動式前導機器魚定位之研究zh_TW
dc.titlePassive Localization of a Leading Robotic Fish Using a Lateral-line Array on its Following Vehicleen
dc.typeThesis
dc.date.schoolyear108-1
dc.description.degree碩士
dc.contributor.oralexamcommittee黃千芬,戴璽恆,嚴惟果
dc.subject.keyword群游,反算理論,估測,側線系統,自主水下載具,觀測性矩陣,不確定度分析,zh_TW
dc.subject.keywordfish school,inverse theory,estimation,lateral line system,Autonomous Underwater Vehicle,observability,uncertainty analysis,en
dc.relation.page91
dc.identifier.doi10.6342/NTU201904166
dc.rights.note未授權
dc.date.accepted2019-09-27
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept工程科學及海洋工程學研究所zh_TW
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