臺灣博碩士論文加值系統

針對客製化自駕車，本論文首度提出一種具有在線姿態更新的通用型先進控制技術，以達到具有高精準、高速、大曲率特性的路徑追蹤控制。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、適用於自駕車的新型監督式控制結構，三、適用於路徑追蹤之速度大小和方位角更新之新策略，四、新型之高精準順向路徑定位和逆向路徑規劃。首先，視車輛動力系統為未知系統，根據其特性建立多個模型，建構其對應的完美狀態估計器。結合改良的模型預測控制與新型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，提出了順向路徑定位及逆向路徑規劃，並將其應用於姿態更新方法。根據希爾德雷思(Hildreth)二次規劃，提出一個改良版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優越性。包含三種駕駛場景的模擬，以顯示具輸入限制且多模型改良版之模型預測控制器之控制成效。此外，我們比較了本論文提出的姿態更新策略與MATLAB提供之知名的pure pursuit方法之間的性能。

A universal advanced control technique for customized autonomous vehicles is newly proposed in this thesis, such that a precisely high-speed and large curvature path-tracking servo control with an online pose updating for autonomous vehicles can be achieved. This includes (i) the multi-model-based modified model predictive control (MPC) with input constraints, (ii) a new supervisory control structure for autonomous vehicles, (iii) a new strategy for magnitude of velocity and heading angle updating for path following, and (iv) a new and precise forward-path locating and inverse-path planning. First, the vehicle dynamic system is regarded as an unknown system, and multiple models are established according to its characteristics to construct corresponding perfect state estimators. Then, the modified MPC is combined with the new supervisory control structure to track a given path and activate the pose updating online in order to have a precise path-tracking performance. Furthermore, the forward-path locating and the inverse-path planning are proposed and applied to the pose-updating method, and based on Hildreth’s quadratic programming, a modified observer-based MPC with input constraints is presented. Finally, use the Vehicle Dynamics-MATLAB ＆ Simulink-MathWorks 2018a as a verified system to verify the superiority of the proposed method. This involves simulations of three driving scenarios to show the control performance of the multi-model-based modified MPC with input constraints. Besides, we compare the performance between the pose updating strategy proposed by this thesis and the well-known pure pursuit method supported by MATLAB to show the merits of the proposed approach.

摘要 I
Abstract II
Acknowledgement III
List of Contents IV
List of Figures VI
List of Tables X
Chapter 1 Introduction 1
Chapter 2 Vehicle Dynamic System and Mathematical Modeling 4
2.1. Vehicle dynamic system 4
2.2. Mathematical modeling 8
Chapter 3 Modified Input-Constrained Model Prediction Control 11
3.1. Model predictive control 12
3.2. Input-constrained model predictive control 14
3.3. Modified observer-based model predictive control 17
3.4. Hildreth’s quadratic programming 20
Chapter 4 Pose Updating 23
4.1. Forward path locating and inverse path planning 24
4.1.1. Forward path locating (FPL) 24
4.1.2. Inverse path planning (IPP) 24
4.1.3. Illustrative examples 25
4.2. Pose updating 30
Chapter 5 Supervisory Control Structure 35
5.1. Simulator for vehicle dynamic system 36
5.2. Supervisory control structure 36
Chapter 6 Illustrative Examples 41
6.1. System identification for vehicle dynamic systems based on the
observer/Kalman filter identification method 42
6.2. Perfect state estimator for vehicle dynamic system 43
6.3. Multi-model-based modified model predictive control with input constraints
for vehicle dynamic system with pose updating 48
Example 6.1: Urban speed scenario 56
Example 6.2: Slow-medium-high-speed scenario for Path 1 64
Example 6.3: Slow-medium-high-speed scenario for Path 2 71
6.4. Comparison between pose updating method and pure pursuit method 78
6.4.1. Pure pursuit block 78
6.4.2. Applying pure pursuit block in Example 6.1 79
6.4.3. Simulation results 80
Chapter 7 Conclusion 86
Reference 87

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