數據化最佳控制設計的特色是不需要知道受控系統的數學式，而是直接利用開迴路實驗數據作控制器設計，但是開迴路實驗數據的量測可能因為雜訊會造成設計的誤差，因此引進了一般性自迴序列濾除器，目地是利用其序列濾除特性，改良數據化最佳控制設計公式，降低雜訊滋生誤差。但是濾除器本身亦會造成低頻函數誤差，因此為了避免低頻函數誤差讓引進濾除器改良控制器準確性的目地造成反效果，必需根據雜訊滋生誤差和低頻函數誤差的關係，去調整濾除器在改良數據化最佳控制設計公式的參數，再與傳統降低誤差的方法作比較，評估利用改良數據化最佳控制設計公式是否較佳。

　　Data based controller synthesis (DBCS) can be used to design a controller directly from the open loop plant test data without explicit knowledge of the plant model. However, design error may occur when the data used contain measurement noise. In order to reduce this error, called noise-induced-error (NIE), an auto-regressive (AR) sequence annihilator was introduced into the DBCS. The use of AR sequence annihilator, however, may produce the low frequency function error (LFE) on the resulting design. Therefore, the parameters of AR sequence annihilator should be adjusted so that the low frequency function error does not exceed the noise-induced-error. A merit of the modified DBCS design is measured by comparing its result with that of the more conventional approach of noise-reduction design; for instance, averaging multiple data from repeated test.

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