本研究提出對於冷卻扇系統一套完整的系統建模、鑑別與控制流程，並且透過預測轉速建立容許誤差範圍，達到即時監控冷卻扇狀態的目的。首先，基於空氣動力學與牛頓力學建立系統之數學模型，同時為了掌握系統特性，參數鑑別有其必要性。據此，先將動態模型以雙線性轉換映射到離散時域，再利用常見的最小平方法進行資料擬合，以取得一組最佳參數，然而，此最佳解可能受雜訊的影響造成擬合失真，此時可藉由最小差距濾波器(Minimum Difference Filter，MDF)獲得改善。除此之外，本研究對於參數鑑別有更進一步之改良，首先根據最小平方法之最佳解，再以萊文貝格－馬夸特方法的迭代技巧，可找到另一組最佳參數達到最小擬合誤差，並且對系統的描述更加完備。而有了這此組最佳參數，本研究即可進行冷卻扇之轉速預測與控制。轉速動態監控方面是基於模型預測，並建立轉速的容許誤差範圍，針對進氣口遭遮蔽的障誤或是驅動電路老化異常的狀況下進行診斷，且以LED燈號即時發出警示，讓使用者提早採取應變措施。另一方面，轉速控制則是採用非線性PI控制器，由於系統內部存在不確定性，且本身受外部擾動及雜訊的影響，藉由選擇適當的切換增益可達到抑制，同時增加控制器的強健性，以達到期望轉速。最後，本研究以模擬確立此套流程的正確性，再藉由實驗驗證其可行性，達到對冷卻扇系統即時監控的完整性。

This research proposes a full procedure of modelling, system identification, control for a cooling fan system. First of all, a physical model is established based on the aerodynamics. Secondly, to carry out the parameter identification, discretize the model by the bilinear transform. With the aid of the minimum difference filter(MDF), the noise effect to the least square regressors can be reduced. Though the least square solution provides an optimal set of parameters, there exists difference between the real and the fitting data. Therefore, seek the iteration of the Levenberg–Marquardt algorithm and the system identification will be improved. With an optimal set of parameters, the speed prediction and fault diagnosis can be realized. By setting an upper and lower bound for the predicted speed, it is convenient to monitor the status of a cooling fan system. In this paper, the covered inlet is assumed to be a fault and can be diagnosed by the abnormal speed. Furthermore, with the nonlinear PI type controller, it is possible for the fan to reach a desired speed subject to the model uncertainty and disturbance. Last but not least, simulation and experiment are performed to verify the feasibility and applicability of the proposed approach.

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