數據化最佳控制(DBLQ)設計，解決了傳統最佳控制(LQ)所面臨的兩個重要問題：系統的狀態必須隨時可得，以及系統數學模式的估測。然而數據化設計的過程中，雜訊對控制器設計的正確性影響甚鉅。為了降低雜訊所造成的誤差，本論文提出改良數據化最佳控制設計法，一般而言，雜訊所造成的設計誤差，將使得最佳解不符合自迴序列的型式，但是真正最佳解乃是符合自迴序列的條件，於是我們引進一般性自迴序列濾除器，利用其序列濾除的特性，使得最佳解都保有自迴序列的型式，以此改良數據化最佳控制設計公式，降低雜訊對數據化設計的影響。

　　The data-based linear quadratic (DBLQ) design, solve two major problems in the linear quadratic (LQ) design: the need for the information of the system states, and the need for an indefinite plant model. With DBLQ design, the controller can be calculated from the open-loop plant test data directly. However, any noise signal in the test data induces error into this DBLQ design, In general, the true LQ solution would be an auto-regressive(AR) sequence, but noise-induced error causes the DBLQ solution deviate from an AR sequence. In order to repress this design error, a modified DBLQ design was developed in this thesis via incorporate an AR sequence annihilator and the DBLQ design.

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