針對於客製化自駕車，本論文根據最佳化當中的隨機搜尋，以改善模型建立的速度。本控制原型的主要技術包括一、具有輸入限制的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、具有通用性的多系統模型之模型選擇機制，四、動態調整路徑的路徑更新機制。由於車輛動力系統是未知的，須根據系統特性建立多個相應的數學模型，透過本論文提出適用於模型建立的演算法，可以大幅度的縮減模型建立的時間。因為要利用多個線性模型來控制自駕車，所以在模型切換的過程中不但要能快速切換而且系統狀態也不能變化的太過於劇烈，透過本論文提出的切換方式，可以有效的消除系統切換所造成控制訊號的震盪。最後，我們使用Vehicle Dynamics-MATLAB & Simulink-MathWorks 2018a 作為驗證本論文所提出方法的可行性。我們測試了四種駕駛場景，其中包含兩種駕駛場景是透過Google MAP所提供的真實路徑。

Aiming at customized autonomous vehicle, this thesis improves the speed of model building based on random search in optimization. The main technology of this control prototype includes (i) an improved model predictive control (MPC) with input restrictions, (ii) an improved supervisory control structure suitable for autonomous vehicle, (iii) a universal model selection mechanism to select multi-system model, and (iv) the path update mechanism for dynamically adjusting the path. Since the vehicle power system is unknown, a number of corresponding mathematical models must be established according to the characteristics of the system. This thesis proposes an algorithm suitable for model identifying, which can greatly reduce the time for model identifying. Because multiple linear models are used to control the autonomous vehicle, not only must it be able to switch quickly during model switching, but the state of system must not change too drastically. The switching method proposed in this thesis can effectively eliminate the oscillation of the control signal. Finally, we use Vehicle Dynamics-MATLAB & Simulink-MathWorks 2018a to verify whether the method proposed in this thesis is feasible. We tested four driving scenarios, including two driving scenarios that are real paths provided through Google MAP.

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