中文摘要
控制器的特色是可使受控體在不確定外擾作用及系統參數變動情況下，仍可維持系統的穩定性與強健性。而模糊控制器的優點則是可以使用知識庫的模糊規則來設計控制器，不需要明確的數學模式。本文結合模糊理論與 控制理論的優點，而發展出穩定且具有高性能的模糊- 控制器。控制的對象是一個兩軸機械手臂系統，當給予一個參考輸入時，欲使受控系統能追蹤參考輸入狀態，吾人使用 控制理論。而系統在轉換成標準型式的 狀態空間表示時，使用T-S模糊推論規則，減少受控系統由非線性轉為線性時，所產生的誤差。在系統推導過程中，將標準狀態空間動態方程式加入模糊理論，轉換成模糊化的狀態空間表示法，再分別利用李爾普諾夫穩定定理，證明加入控制器和觀測器時，閉迴路系統的穩定性。
在做電腦模擬時，由於兩軸機械手臂系統是一個多輸入多輸出的控制系統，而且持續有干擾影響下，吾人選用了一個伺服補償器，目的在於使受控系統能排除干擾而精確追蹤，並且選用一個濾波器，目的在於濾掉沒用的訊號，而電腦模擬結果顯示，穩定誤差維持在很小的範圍內，此說明了模糊- 控制器的可行性和精確性。

Abstract
The characteristic of the H∞ controller is maintaining the stability and robustness when the uncertainly disturbances and the parameters of the system changes. The advantage of the fuzzy controller is able to use the rule of expert's knowledge base system to design the fuzzy controller, and do not need clear mathematic equations. This paper combines the advantages of the fuzzy theory and the H∞ control theory to develop a steady and high performance Fuzzy-H∞ controller. The plant that we want to control is the mechanical arm control system of two axles (two-link robot).When giving a reference input model, we use H∞ control theory to track the states of two-link robot. The system is changed into the standard state space model form, and we can use T-S fuzzy inference rules to reduce the error which produced by the process of nonlinear to linear system. In the course of deriving the system equations, we can join the fuzzy theory to the standard state space dynamic equations, to change the expression of the fuzzy state space. And use the Lyapunov stability theorem to prove the stability of the closed-loop system when joining the Fuzzy-H∞ controller and observer.

While doing computer simulation, because the two–link mechanical arm system is MIMO system, and undergo continuously disturbances, we select a servo compensator of the purpose to reject the disturbances and tracking accurately, and select a filter of the purpose to filter out the useless signals, and the computer simulation results show that the steady state error maintain in a very small range, and this proves the feasibility and accuracy of the Fuzzy-H∞ controller.

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