為了改善傳統求取PI控制器參數設定的方法，在本研究中我們發展出了新的線上控制器調諧策略。具體而言，我們提出系統化的步驟來建構雙層式的模糊推論系統，以便鑑認干擾的來源及大小，再據以挑選適當的控制器參數。我們利用系統的有向圖模式，預測任一給定干擾所有可能出現的線上症候，並依此編譯成模糊診斷法則。至於控制器參數是根據上述失誤發生可能性指標來調整。由於控制器的動作是可以滿足各種不同干擾的需求，我們相信這樣的調諧策略應可以有效提升控制品質。而此一構想之可行性也在模擬案例中得到驗證。

　A novel on-line tuning strategy is developed in this work to enhance the control performance achieved by adopting the PI setting identified with any traditional method. Systematic procedures are devised for constructing a two-layer fuzzy inference system to diagnose the locations and magnitudes of disturbances, and also to select the appropriate controller parameters accordingly. The fuzzy diagnosis rules of a given disturbance are derived from all possible patterns of the resulting on-line symptoms and these patterns can be predicted on the basis of the system digraph model. The controller settings are then adjusted on-line according to the fault occurrence index. This tuning strategy is believed to be effective due to the fact that the controller actions can be tailored to satisfy the distinct needs of each and every frequently-occurred disturbance. Extensive simulation studies have been carry out to substantiate this proposition.

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