本論文目的為建構一台自駕自行車，使其能自我平衡並於校園道路自動駕駛。首先根據自行車的系統動態分析，設計平衡控制器，使其能對車速的變化具有強健性，以實現自我平衡並在遙控狀態下能直線行駛或轉彎。在確保了底層的平衡控制之後，進行上層控制的感測融合與路徑追蹤。感測融合部分採用基於線性化的擴展式卡爾曼濾波器對GNSS模組、IMU等感測訊號做融合，以獲得精確與快速定位回授資訊。路徑追蹤控制則是基於先在給定路徑上產生一台虛擬的車輛運動，再控制自駕自行車與虛擬車輛之間的位移與角度誤差，使自駕自行車能夠行駛於給定的路徑上且能夠以特定速度軌跡完成路徑追蹤，論文中也對追蹤控制的強健性與誤差性收斂提供了理論證明。除了模擬驗證外，自駕自行車也於校園進行測試。路測實驗中自行車達到1.5m/s至4.0m/s的速度，與虛擬車輛之間的追蹤誤差小於18.0公分。

The purpose of this research is to construct a self-driving bicycle that can balance itself and drive automatically on campus roads. First, based on the dynamic analysis of the bicycle system, a balance controller, which is robust to changes in bicycle speed, is designed to achieve self-balancing and allow the bicycle to be driven under remote control. After ensuring the performance of the lower-level balance control, the high-level control, which consists of sensor fusion and path following parts, are studied. The sensor fusion part uses extended Kalman filter to fuse sensor signals from GNSS module and IMU’s to obtain accurate and fast localization information for feedback purposes. Path following control is based on a pre-generated virtual vehicle motion on a given path. The displacement and angle errors between the self-driving bicycle and the virtual vehicle are controlled so that the real bicycle can drive by itself on the given path with a specified speed trajectory. The robust stability and convergence of the path tracking control algorithm are rigorously proved. In addition to simulation verification, self-driving bicycle is also tested on campus roads. In the road test experiment, the bicycle reached a speed of 1.5m/s to 4.0m/s, and the tracking error with the virtual vehicle was less than 18.0 cm.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
名詞代號與常用符號一覽表 viii
1. 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 3
1.2.1 自行車建模與控制 4
1.2.2 自駕車技術 6
1.3 論文簡介 9
2. 側向動力學模型 10
2.1 定義系統參數 10
2.2 運動學分析 11
2.3 動力學分析 12
2.4 控制設計的狀態空間模型 13
3. 平衡控制與感測 16
3.1 強健平衡控制與設計 16
3.2 自行車轉向控制 19
3.3 自行車平衡模擬 20
4. 感測融合 23
4.1 感測融合與定位 23
4.2 路徑軌跡 28
4.3 EKF感測融合模擬 28
5. 路徑追蹤控制 31
5.1 路徑追蹤控制演算法 31
5.2 路徑追蹤控制模擬 36
6. 系統架構及硬體介紹 40
6.1 自行車硬體系統架構 40
6.2 慣性感測器 44
6.3 伺服馬達 45
6.4 GNSS模組 46
7. 實驗結果與數據 48
7.1 GNSS模組實驗結果 48
7.2 平衡控制實驗結果 49
7.3 轉彎控制實驗結果 52
7.4 感測融合實驗結果 53
7.5 路徑追蹤實驗結果 55
8. 結論與未來工作 59
8.1 結論 59
8.2 未來工作 60
9. 參考文獻 61

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