本論文提出一種新穎的控制方法，為即時適應T-S模糊類神經模型，並透過此方法去鑑別未知的動態系統。其中，強健控制器是設計用來補償模型誤差和外界干擾。由於大部分的系統都是非線性，因此本論文使用均值定理，將一個非線性系統轉至為虛擬線性系統，然後T-S模糊類神經模型便能近似這些未知的動態系統。在一個未知系統中，T-S模糊神經網絡模型是一種有效的鑑別方法。但是T-S模糊神經模型在處理一個高階系統時，由於模糊規則數過於龐大，將耗費大量運算時間。因此，我們提出一種層次的架構，將複雜的模糊類神經網路架構，分成數個低階的子系統，以降低模糊規則數與減少運算時間。最後，本文舉出一些affine非線性系統的例子，由模擬結果可以證明，該控制器的設計呈現良好的性能和效果。

This dissertation proposes a novel control method for identification of a class of uncertain systems by using on-line adaptive T-S fuzzy-neural modeling. And the robust controller is designed to compensator modeling errors and external disturbances. This dissertation uses the mean value theorem to transform the nonlinear system dynamic into a virtual linear system because the most systems are nonlinear. Then the T-S fuzzy-neural model can identify the dynamic model of the linearized system. Although T-S fuzzy-neural modeling is an efficient identification method for uncertain systems, it encounters serious problem of fuzzy rules explosion in processing a high dimensional system. Furthermore, this problem leads to large computing time. Therefore, we propose a kind of hierarchical structure through which the complex structure of fuzzy-neural networks can be modeled by using a family of subsystems with fewer dimensions. By this hierarchical structure, the fuzzy rules and the computation time will decrease. Finally, this dissertation gives some examples for affine nonlinear systems, and the simulation results illustrate that the proposed controller design presents good performances and effectiveness.

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