本論文設計一套電動輔助自行車的智慧型控制器，進行騎乘情況的模擬與分析，以提升其騎乘效率。灰模糊控制系統是以灰色理論結合模糊理論為設計基礎，用灰色理論中的灰色決策概念，以行車速度、曲柄轉速及踩踏扭力來判別騎乘狀態(如平路、上坡和下坡)，之後再由模糊控制運算，有效控制輪轂馬達輸出，減少電池電能的浪費，達到節能效果，提升續航里程。 研究中以Matlab軟體來撰寫程式並進行模擬，經單車訓練平台與實際道路的實驗結果，來設計不同的模擬範例，並以灰模糊控制和傳統扭力控制之模擬結果與實驗結果做比較，證實模擬結果的可信度，能使用灰模糊程式的模擬結果來估計總騎乘時間和騎乘里程，藉此找出最佳的控制參數，減少做道路實驗之麻煩，節省人力與時間的成本。此外，也在灰模糊控制系統裡，加入倒傳遞類神經網路來預測控制器輸出到驅動器之修正電壓訊號，使荷重輕的騎乘者騎乘時，不會有輔助動力過大的問題，避免發生類似暴衝之危險，確保騎乘安全。 The thesis is to design the intelligent control system for the electric assisted bicycle to increase the riding efficiency by simulation and analysis. The control system is developed by using the grey theory and fuzzy theory together. The riding mode is set up by the riding speed, spin speed and torque signals base on the developed grey decision-making program. Then, the fuzzy controller will send out the optimal voltage output to the motor in order to save more electric energy and increase the total cycling mileage. In the research, the control program is designed by using the Matlab. There are several different simulating examples which are decided by the pre-experiments of both bicycle training platform and actual road test. Then, the simulated results are compared with the experimental results. The comparing results show that this developed intelligent controller work very well. Finally, the simulated results can be apply to estimate the total cycling time and the total cycling mileage. It is the advantage for the future application to avoid lots of actual road test. By the way, the Artificial Neural Network(ANN) theory is applied in this grey-fuzzy controller together to compensate the output voltage for the riders with different weight in order to save more electric energy and prevent any dangerous situation.