本論文主在探討基因演算法於直流馬達之模型建立及伺服控制之實務應用研究。首先探討基因演算法應用於建立受控體之模型，並且配合物理之原則量測馬達內部之參數，將量測設備之成本降到最低並且提高模型之可靠度。
工業上於控制器之選用多採比例積分微分控制器(PID Controller)為主，但是其參數決定方法仍多採人為的嘗試錯誤法，不但耗時且效率差。論文中將基因演算法運用於PID控制器之參數搜尋上，主以PID-GA、MRPID-GA及嵌入式基因演算法三種進行參數之搜尋。由實驗結果顯示應用基因演算法於PID控制器之參數搜尋上不但可協助控制器參數設計人員完成設計，更將複雜的參數決定過程簡化至只需決定所想要的輸出響應。另外嵌入式基因演算法乃是實現於單晶片之硬體中，於實務上不但大幅度降低設備成本且不必事前得知受控體之數學模型，更提升控制程式與控制參數之保密性。

The main purpose of the thesis is practically applying genetic algorithm (GA) to establish D.C. motor model and servo position control. First, GA is aimed at modeling. We propose several Physics principle to measure the parameter inside the motor in order to save the cost of measuring equipment and promote the reliability of the model.
The majority of the regulators used in industry are of the PID type. However, the most popular tuning way is manual trial-and-error method and it is laborious and time consuming. By means of GA, three methods, PID-GA、MRPID-GA, and Embedded GA, are used in the thesis for searching the parameter of PID controller. The experiment of the result shows that GA is not only helping engineers design the controller but also simplifying the complex designing process to only determine the desired response. Furthermore, being realized in single-chip hardware, Embedded GA method saves the cost of equipment, does not need well-known mathematic model in advance, and promotes the security of control program and parameter.

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