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研究生:劉保良
研究生(外文):Bao-LiangLiu
論文名稱:適用於客製化自駕車的先進控制器原型與驗證
論文名稱(外文):Prototype and Verification of Advanced Controller for Customized Autonomous Vehicle
指導教授:蔡聖鴻
指導教授(外文):Jason Sheng-Hong Tsai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:108
中文關鍵詞:自駕車模型預測控制監督式控制架構觀測/卡爾曼濾波器系統辨識法姿態更新
外文關鍵詞:Autonomous vehiclemodel predictive controlmodified supervisory control structureobserver/Kalman filter system identificationpose updating
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針對各種客製化自駕車,文獻已提出了具有在線姿態更新的控制方法,以達到高精準的路徑追蹤控制,本論文所提的技術延伸了文獻的控制方法,基於技術就緒指數第六級(即在相關環境驗證系統或子系統或原型)的概念,製作了一個客製化自駕車的先進控制器原型。本控制器原型的主要技術包括一、具有輸入限制且多模型之改良版模型預測控制(MPC),二、適用於自駕車的改良版監督式控制結構,三、一種新的多系統模型之模型抉擇序列機制,四、應用於高度複雜之路線模擬,以達到在相關環境驗證系統或子系統或原型。因為車輛動力系統為未知系統,須根據其特性建立多個模型,建構其對應的完美狀態估計器。利用改良的模型預測控制與改良的監督控制結構,以利追蹤理想路徑,並開啟在線的姿態更新,以具有精確的路徑追蹤性能。而後依據希爾德雷思 (Hildreth) 二次規劃,使用了一個改良版且基於觀測器的模型預測控制。最後,我們使用Vehicle Dynamics-MATLAB & Simulink-MathWorks 2018a作為驗證工具來驗證所提方法的可行性。我們測試了六種駕駛場景,其中包含三種從Google Map經處理過後所得到之真實路徑,以顯示所製作的客製化自駕車其先進控制器原型的成效。
For customized autonomous vehicles, the control method with the online pose updating to achieve a high-precision path-tracking control has been presented in the literature. This thesis extends the presented technology under the concept of Level 6 of Technology Readiness Index (i.e., system/subsystem model or prototype demonstration in a relevant environment) to create a prototype of an advanced controller for a customized autonomous vehicle. Main technologies of this prototype include (i) the multi-model-based modified model predictive control (MPC) with input constraints, (ii) a modified supervisory control structure for autonomous vehicles, (iii) a new mechanism for selections of multi-system model sequences, and (iv) a verification for highly complex paths to have a system/subsystem model or prototype demonstration in a relevant environment. Since the vehicle dynamic system is an unknown system, multiple models are established according to its characteristics to construct corresponding perfect state estimators. The modified MPC is combined with the modified supervisory control structure to track a given path and activate the pose updating online to have a precise path-tracking performance. Then, based on Hildreth’s quadratic programming, a modified observer-based MPC with input constraints is utilized for the proposed method. In the end, we use the Vehicle Dynamics-MATLAB & Simulink-MathWorks 2018a as a verified system to verify the feasibility of the proposed method. We apply to six driving scenarios, including three real paths obtained from Google Map after processing, to show the effectiveness of the created prototype of an advanced controller for a customized autonomous vehicle.
摘要 I
Abstract II
Acknowledgement III
List of Contents IV
List of Figures VI
List of Tables XIII
Chapter 1 Introduction 1
Chapter 2 Model Prediction Control with Modified Input Constrain 4
2.1. Model predictive control 5
2.2. Input-constrained model predictive control 7
2.3. Modified observer-based model predictive control 10
2.4. Hildreth’s quadratic programming 13
Chapter 3 Modfied Supervisory Control Structure 16
3.1. Simulator for vehicle dynamic system 17
3.2. Modified supervisory control structure 17
Chapter 4 Parameters of Model and Sequences of Model Selections 22
4.1 Parameters of models 23
4.2 Sequences of model selections for vehicle and simulator 35
4.2.1 Pre-selected model sequence for vehicle 35
4.2.2 Pre-selected model sequence for simulator 39
4.3 Sequences of model selections for vehicle and simulator 40
Chapter 5 Verification 44
5.1 Fictitious paths obtained from vehicle dynamics blockset-MATLAB 49
5.2 Real paths transferred from Google Map 72
Chapter 6 Conclusion 104
Reference 106
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