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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蘇偉儁 | zh_TW |
| dc.contributor.advisor | Wei-Jiun Su | en |
| dc.contributor.author | 楊宜瑄 | zh_TW |
| dc.contributor.author | Yi-Syuan Yang | en |
| dc.date.accessioned | 2025-08-05T16:18:36Z | - |
| dc.date.available | 2025-08-06 | - |
| dc.date.copyright | 2025-08-05 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2025-06-11 | - |
| dc.identifier.citation | [1] N. H. T. S. Administration, "Traffic safety facts 2021: A compilation of motor vehicle traffic crash data," National Center for Statistics and Analysis, 2023.
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Bishop, "Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960," 1994. [45] L. Nelson and E. Stear, "The simultaneous on-line estimation of parameters and states in linear systems," IEEE Transactions on automatic Control, vol. 21, no. 1, pp. 94-98, 1976. [46] SAE J670 Vehicle Dynamics Terminology, 1952, 2022. [47] I. The MathWorks, "Vehicle Dynamics Blockset R2024a," ed: The MathWorks, Inc., 2024. [48] A. B. Will and S. H. Z˚Ak, "Modelling and Control of an Automated Vehicle," Vehicle System Dynamics, vol. 27, no. 3, pp. 131-155, 1997, doi: 10.1080/00423119708969326. [49] H. B. Pacejka, "Tire and Vehicle Dynamics, 3rd Edition," (in English), Tire and Vehicle Dynamics, 3rd Edition, pp. 1-632, 2012. [Online]. Available: <Go to ISI>://WOS:000322044900016. [50] "dSPACE ASM," ed: dSPACE GmbH, 2024. [51] X. Niu, Y. Ban, Q. Zhang, T. Zhang, H. Zhang, and J. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98421 | - |
| dc.description.abstract | 本研究旨在探討狀態估測以及參數識別對於車輛模型的重要性，強調其在加強先進駕駛輔助系統 (ADAS) 性能方面的作用。透過回顧相關文獻中即時估測的方法，包括卡爾曼濾波及其最新進展，本研究評估了這些方法的適用性、優點和局限性。研究中所提出演算法利用一個四自由度的車輛模型，結合Magic Formula輪胎模型，並採用了離散化空間狀態模型來進行狀態與參數估測。由於擴展卡爾曼濾波(EKF)具計算效率佳和適用於複雜系統等優點，因此以雙擴展卡爾曼濾波(DEKF)為框架設計估測演算法。此基於雙擴展卡爾曼濾波的估測方法於MATLAB/ Simulink 環境中進行模擬。該研究模擬使用 dSPACE ASM 模擬軟體，以多體虛擬車輛替代實車測試。本篇研究中詳盡說明測試案例、感測器規格和過程中使用的模擬工況，以及它們與研究目標的相關性。模擬結果顯示，所提出的狀態與參數識別方法能夠改善車輛在不同情況下的狀態估測精度。 | zh_TW |
| dc.description.abstract | This research investigates the significance of state estimation, with a specific focus on parameter identification techniques, emphasizing its use in Advanced Driver Assistance Systems (ADAS). Through a comprehensive review of real-time estimation methods, including Kalman filtering and recent advancements, assessing their applicability, advantages, and limitations in relevant literature. The proposed approach utilizes a 4-degree-of-freedom vehicle model, incorporating the tire magic formula model and employing discretization techniques. The Dual Extended Kalman Filter (DEKF) framework is proposed as the primary estimation method, and is implemented using MATLAB/Simulink environment. The research justifies the use of dSPACE ASM as a surrogate for real-world testing, detailing test cases, sensor specifications, maneuvers, and their relevance to research objectives. Results demonstrate efficacy of the proposed methodology in parameter identification and its enhancing effect on vehicle state estimation accuracy across various maneuvers. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-05T16:18:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-05T16:18:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Abstract ii
摘要 iii Contents iv List of figures ix List of tables xiii Nomenclature xiv Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Motivation 2 1.3 Organization of this Thesis 3 Chapter 2 Literature Review 5 2.1 Vehicle Models 6 2.1.1 Kinematic Models 6 2.1.2 Dynamic Models 6 2.2 Sensor Configurations 8 2.2.1 Steering Angle Sensors 8 2.2.2 Wheel Speed Sensors 9 2.2.3 GPS/GNSS 9 2.2.4 Inertial Measurement Unit (IMU) 9 2.3 State and Parameter Estimation 10 2.3.1 Tire Forces 12 2.3.2 Sideslip Angle/ Cornering Stiffness 13 2.3.3 Velocity 14 2.3.4 Roll Analysis 15 2.3.5 Vehicle Parameter Identification 16 2.3.6 State and Parameter Estimation 17 2.4 Estimation Methodology 18 2.4.1 Kalman Filtering 18 2.4.2 Other Methods 20 2.5 Conclusion of Literature Review 21 Chapter 3 Dual Extended Kalman Filter 23 3.1 Dynamics Systems 23 3.1.1 Linear Dynamic Systems 23 3.1.2 Nonlinear Dynamic Systems 23 3.1.3 Discretization of Continuous Systems 24 3.1.4 Linearization of Dynamic Systems 25 3.2 Kalman Filter 26 3.2.1 History of the Kalman Filter 26 3.2.2 Concept of the Kalman Filter 26 3.2.3 Equations of a Kalman Filter 28 3.2.4 Extended Kalman Filter 30 3.3 Dual Extended Kalman Filter 32 Chapter 4 Vehicle model 36 4.1 4-DOF Vehicle Model 36 4.1.1 Vehicle Coordinates: 36 4.1.2 Vehicle Dynamic Equations 37 4.2 Tire Model 43 4.2.1 Tire Coordinate System 43 4.2.2 The Magic Formula Tire Model 45 4.2.3 Full Set of Tire Modeling Equations 47 4.3 Estimator Settings 52 4.3.1 Weighting Covariance 52 4.3.2 Relationship Between Parameters 53 Chapter 5 Simulation 56 5.1 Simulation Structure 56 5.2 Simulation Environment 58 5.3 Simulation Settings 59 5.3.1 Vehicle Loads 59 5.3.2 Sensor Configuration 61 5.3.3 Maneuvers 63 Chapter 6 Results 66 6.1 State Estimation 66 6.1.1 Longitudinal and Lateral Acceleration 66 6.1.2 Roll and Yaw Rates 68 6.1.3 Sideslip Angle 70 6.2 Parameter Identification 72 6.2.1 Mass Estimates 72 6.2.2 Inertial Parameter Estimates 75 6.3 State Estimation with Parameter Update 76 6.3.1 Double Lane Change Maneuver 77 6.3.2 Constant Radius Maneuver 82 Chapter 7 Conclusions and Future Work 87 7.1 Conclusions 87 7.2 Future Works 88 References 90 Appendix A 96 Appendix B 97 | - |
| dc.language.iso | en | - |
| dc.subject | 車輛參數估計 | zh_TW |
| dc.subject | 車輛狀態估測 | zh_TW |
| dc.subject | 雙擴展卡爾曼濾波 | zh_TW |
| dc.subject | Dual Extended Kalman filter | en |
| dc.subject | Vehicle state estimation | en |
| dc.subject | Vehicle parameter estimation | en |
| dc.title | 載重車輛的即時狀態估測與參數識別 | zh_TW |
| dc.title | Real-time State Estimation and Parameter Identification of Load-carrying Vehicles Using a Dual Kalman Filter | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 詹魁元;陳明彥 | zh_TW |
| dc.contributor.oralexamcommittee | Kuei-Yuan Chan;Ming-Yen Chen | en |
| dc.subject.keyword | 車輛狀態估測,車輛參數估計,雙擴展卡爾曼濾波, | zh_TW |
| dc.subject.keyword | Vehicle state estimation,Vehicle parameter estimation,Dual Extended Kalman filter, | en |
| dc.relation.page | 100 | - |
| dc.identifier.doi | 10.6342/NTU202403171 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-06-11 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | 2025-08-06 | - |
| Appears in Collections: | 機械工程學系 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf | 3.47 MB | Adobe PDF | View/Open |
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