本篇論文的目的，在於藉由模擬來驗證數據化控制器設計(DBCS)與傳統上先進行系統識別後在作控制器設計，兩種設計方法的優劣。DBCS跟過去的控制理論不同的地方，在於過去的控制器設計，必須先透過系統識別的步驟將系統模式估測出來，然後再根據所估測的系統模式來設計控制器。至於DBCS的控制設計，則是透過實驗數據，在不知道系統模式的前題之下，直接把控制器計算出來。自從DBCS發展出來後，我們一直未曾對這兩種不同的設計方式予以比較。我們有興趣的是，在實驗數據裡頭含有雜訊或錯誤訊號的情形之下，比較DBCS跟過去的控制器設計那一個會有較佳的性能。

　　The difference between data-based controller synthesis (DBCS) and traditional control theory is that DBCS does not need system identification to build the parameterized plant model. With DBCS, the controller can be directly calculated from the open loop plant test data even thought the parameterized plant model has not yet been identified. But the traditional control theory synthesizes the controller through the parameterized plant model which is identified by the system identification technique. Since the DBCS was developed, we have not yet compared the performance of the DBCS and traditional control theory. What we are interested is which design method get the better performance when open loop plant test data contain the noise. The purpose of this research is to verify the performance of DBCS as compared with the traditional control theory through the numerical experiment.

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