跳到主要內容

臺灣博碩士論文加值系統

(44.192.94.177) 您好!臺灣時間:2024/07/21 18:38
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:周士閔
研究生(外文):Shih-Min Chou
論文名稱:潛艦系統鑑定之最佳輸入設計
論文名稱(外文):Optimal Input Design for Submarine System Identification
指導教授:郭振華郭振華引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:93
語文別:英文
論文頁數:64
中文關鍵詞:系統鑑定最佳輸入水下載具
外文關鍵詞:Underwater VehiclesSystem identificationOptimal inputs
相關次數:
  • 被引用被引用:0
  • 點閱點閱:185
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
潛艦為因應不同深度航行,避敵或攻擊之需要,必須具備高度的操控性能,而其主要基礎則在於建立掌握潛艦操控運動數學模式及其相關流體動力係數之能力,所以在潛艦運動數學模式中,流體動力係數的正確性對於潛艦的控制操控性能影響甚鉅。本篇論文以非線性估測器理論為基礎提出對潛艦的流體動力係數作即時系統識別的研究,實際結合待估測的未知參數與雜訊的關係,降低系統識別的不確定性。此外,提出最佳輸入設計,在即時系統識別過程中,控制潛艦運動的路徑有利於提昇識別參數的準確性。最後利用模擬比較最佳輸入與隨機白色二值輸入(PRBS)證明本文所提出潛艦系統鑑定之最佳輸入設計能夠準確識別潛艦的流體動力係數。
The accuracy of hydrodynamic coefficients in a submarine vehicle’s dynamic model strongly affects the dynamic performance of its control system. This thesis presents a mutual-information-based observability metric for the on-line dynamic parameter identification of a submarine vehicle. The method provides practical means of estimation unknown parameters and plant noises using a nonlinear observer, as well as estimating the uncertainty associated with the parameters. A metric for selecting optimal input signals for parameter identification is also proposed. The trajectory of the submarine is controlled so that the identification procedure favors parameters that have the greatest uncertainty at any given time. Optimal rudder/elevator deflections for identifying the hydrodynamic coefficients of the submarine are demonstrated using computer simulations. The estimated values are compared with the Pseudo-Random-Binary-Sequence input signals that are commonly used in system identification. This algorithm for selecting optimal inputs is found to be efficient and robust to noises.
誌謝 I
摘要 II
ABSTRACT III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES IX
LIST OF TABLES IX
LIST OF SYMBOLS X
CHAPTER1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 PURPOSE OF THE THESIS 3
1.3 RELATED WORKS 4
1.4 THESIS ORGANIZATION 5
CHAPTER2 MODELING AND OBSERVER DESIGN 7
2.1 COORDINATE SYSTEMS 8
2.2 THE DYNAMIC MODEL 9
2.3 ESTIMATION PROCESS 12
CHAPTER3 OPTIMAL INPUT DESIGN 16
3.1 OBSERVABILITY METRIC 16
3.2 OPTIMIZATION PROCEDURE 25
CHAPTER4 SIMULATION RESULTS 31
4.1 IDENTIFICATION RESULTS OF HYDRODYNAMIC COEFFICIENTS 31
4.2 PARAMETER OBSERVABILITY 44
4.3 OPTIMIZATION RESULTS 52
CHAPTER5 CONCLUSIONS 57
APPENDIX 59
REFERENCES 63
[1]D. Sen, "A study on sensitivity of maneuverability performance on the hydrodynamic coefficient for submerged bodies," J. Ship Res., vol. 44, No. 3, pp. 186-196, 2000.
[2]M. Gautier and P. Poigent, "Extended Kalman Filtering and Weighted Least Squares Dynamic Identification of Robot," Control Engineering Practice, vol. 9, No. 12, pp. 1361-1342, 2001.
[3]L. R. Ray, "Nonlinear Tire Force Estimation and Road Friction Identification: Simulation and Experiment," Automatica, vol. 33, No.10, pp. 1819-1833, 1997.
[4]A. Gelb, Applied Optimal Estimation. Cambridge MA: MIT, 1974.
[5]V. A. Sujan and S. Dubowsky, "An Optimal Information Method for Mobile Manipulator Dynamic Parameter Identification," IEEE/ASME Transactions on Mechatronics, vol. 2, No 2, pp. 215-225, June 2003.
[6]J. Kim, K. Kim, H. S. Choi, W. Seong, and K.-Y. Lee, "Estimation of Hydrodynamic Coefficients for an AUV Using Nonlinear Observers," IEEE Journal of Oceanic Engineering, vol. 27, pp. 830-840, October 2002.
[7]M. Gautier and W. Khalil, "Exciting Trajectories for the Identification of Base Inertial Parameters of Robots," The International Journal of Robotics Research, vol. 11, No.4, pp. 362-375, August 1992.
[8]R. K. Mehra, "Optimal Inputs for Linear System Identification," IEEE Transactions on Automatic Control, vol. AC-19, No.3, pp. 192-200, June 1974.
[9]R. Serban and J. S. Freeman, "Identification and Identifiability of Unknown Parameters in Multibody Dynamic Systems," Multibody Syst. Dyn, vol. 5, No. 4, pp. 335-350, 2001.
[10]M. Boutayeb and D. Aubry, "A Strong Tracking Extended Kalman Observer for Nonlinear Discrete-Time Systems," IEEE Transactions on Automatic Control, vol. 44, No. 8, pp. 1550-1556, August 1999.
[11]M. Boutayeb, H. Rafaralahy, and M. Darouach, "Convergence Analysis of the Extended Kalman Filter Used as an Observer for Nonlinear Deterministic Discrete -Time Systems," IEEE Transactions on Automatic Control, vol. 42, No.4, pp. 581-586, Aprial 1997.
[12]D. Applebaum, Probability and Information: Cambridge University, 1996.
[13]X. Feng, K. A, Loparo, and Y. Fang, "Optimal State Estimation for Stochastic Systems: an Information Theoretic Approach," IEEE Transactions on Automatic Control, vol. 42, No.6, pp. 711-785, June 1997.
[14]M. Beckerman, "A Bayes-Maximum Entropy Method for Multi-Sensor Data Fusion," presented at Robotics and Automatica, Nice, France, May 1992.
[15]R. C. Smith and P. Cheese, "On the Representation and Estimation of Spatial Uncertainty," The International Journal of Robotics Research, vol. 5, No. 4, pp. 56-68, Winter 1986.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top