本文提出一個使用螞蟻群聚最佳化演算法來調整區間第二類模糊類神經網路的參數，並將其應用於函數近似與非線性系統之適應控制器設計。區間第二類模糊系統涵蓋了第一類模糊系統，使得我們可以掌握更多系統的不確定性。在非線性系統之適應控制過程中，區間第二類模糊類神經控制器的權重値是經由螞蟻群聚最佳化演算法來即時調整，以產生適當的控制輸入。為了即時評估閉迴路系統穩定的趨勢，本文使用李亞普諾夫函數來分析其穩定性。並提出一個能量適應函數於螞蟻群聚最佳化演算法中，藉此獲得較佳的閉迴路系統的穩定度。此外，由於螞蟻群聚最佳化演算法可能在線上即時控制過程中使系統狀態進入不穩定的區域。因此，在控制結構中加入了監督控制，限制系統的狀態在穩定的範圍內。本文藉由電腦模擬結果驗證所提出方法的可行性與效能。最後，將此控制法則應用在直流伺服馬達追蹤控制實驗。

In this thesis, an ant colony optimization algorithm used to tune the parameters of interval type-2 fuzzy neural networks is proposed for function approximation and adaptive control of nonlinear systems. Type-2 fuzzy sets and systems generalize (type-1) fuzzy sets and systems so that more uncertainty can be handled. In adaptive control procedure for nonlinear systems, the weights of the interval type-2 fuzzy neural controller are online adjusted by the ant colony optimization algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop systems, an energy fitness function derived from Lyapunov function is involved in the ant colony optimization algorithm. In addition, the system states may go into the unstable region if the ant colony optimization algorithm can not instantaneously generate the appropriate weights. In order to guarantee the stability of the closed-loop nonlinear system, a supervisory controller is incorporated into the controller. Finally, some computer simulation examples and a servo motor experiment are provided to demonstrate the feasibility and effectiveness of the proposed method.

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