本研究基於學生方程式無人賽車之競賽，將原本已完成在縮小比例實驗載具之演算法，導入實際大小賽車參數進行模擬，同時改良與精進相關演算法。研究主要以建構四輪車輛模型為主，利用參考之載體資訊，再對於模型預測控制進一步進行性能改善。模型預測控制導入扭力引導系統(Torque vectoring system)，來幫助車輛能在更高的速度進行過彎，並避免轉向不足之現象。此外，即時定位與地圖建構演算法將會改良至Fast SLAM2.0來提升自動駕駛之定位能力。

This research is based on Formula Student Driverless competition. The algorithms developed from the scaled-down experimental vehicle are adapted and implemented for the real-sized racing car in the simulation. The algorithms are further improved and refined in this research. The primary focus of this research is to construct a four-wheel drive (4WD) vehicle model and explore the model predictive control (MPC) using information from the racing car. The model predictive control incorporates a torque vectoring system in order to enhance the ability of the vehicle during cornering, which can be at higher speeds and prevent understeering. The Simultaneous Localization and Mapping (SLAM) algorithm will be enhanced by using Fast SLAM 2.0 to improve the localization capability of autonomous driving.

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
名詞縮寫與常用代號對照表 x
1.緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 論文概要 7
2.系統規劃與參考載具 8
2.1 駕駛環境 9
2.2 四輪獨立驅動載具(TH06) 11
2.2.1 電機牽引系統規格 12
2.2.2 懸吊以及轉向裝置規格 13
2.2.3 輪胎規格 16
2.2.4 輪內馬達傳動系統 18
3.模型建構 20
3.1 修正單軌動力學模型 25
3.2 四輪平面動態模型 26
3.3 無側滑角運動學模型 27
3.4 輪胎與地面交互作用力模型 27
3.4.1 輪胎縱向模型 29
3.4.2 輪胎側向模型 30
3.4.3 滾動摩擦 32
3.5 輪胎重量轉移動態(Weight Transfer) 33
3.6 扭矩分配系統 34
3.7 轉向系統建構與整合 36
3.7.1 轉向系統之建構 36
3.7.2 轉向系統之整合 37
3.8 馬達動態的建立和整合 38
3.8.1 馬達動態的建立 38
3.8.2 馬達動態的整合 39
4.導航動態模型建立與控制 40
4.1 動態模型 40
4.2 電機牽引系統限制 42
4.3 隨機最佳化模型預測控制 44
5.定位估測與地圖建構 51
5.1 高速動態估測器 52
5.2 即時定位與地圖建構演算法 54
6.模擬設計與結果 59
6.1 四輪驅動車輛之模型建立與分析 60
6.1.1 四輪驅動車輛模型之建立 60
6.1.2 四輪驅動車輛模型之分析 61
6.2 扭矩分配系統成效之分析 65
6.3 即時定位與地圖建構演算之改進 69
7.結論與未來工作 72
7.1 電機系統之效率測試資料整合 72
7.2 煞車回充系統 72
8.參考文獻 73

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