隨著工業不斷發展，工廠也邁向生產自動化方向。在生產自動化中，為了能提升生產速度，提高產品品質以增加生產競爭力，機械臂成了工廠製造流程中，不可或缺的生產設備。機械臂能夠替代許多以往人力所從事的危險和單調但卻需要高度精密的工作，同時也幫助工廠節省生產成本。
機械臂為一個高度非線性，並且為多輸入多輸出系統；同時機械臂需要能適用在多種不同環境，因此考慮其受到的各種外來干擾，以及系統的參數性質，為機械臂控制研究中一個重要的課題；外來干擾以及系統參數變動或不確定性，將可能導致系統控制效能下降，甚至導致系統不穩定；本文將利用尤拉-拉格朗其法所推導出的機械臂系統，並且在具有外擾以及參數不確定性的情況之下，提出一個由適應參數更新律以及具有抑制外擾能力的 控制器，使的機械臂追蹤誤差能夠漸進收斂至零，已達到實際機械臂控制目的。
最後本文將藉由一個雙關節軸機械臂的電腦模擬作為控制對象，已驗證所設計的適應 控制器之控制情形；並且由模擬結果可知，控制器在適當的參數選取之下，系統具有良好的追蹤能力，同時利用兩個不同的參考輸入，觀察控制器的追蹤情形，可知所設計的適應 控制器均確實的追蹤到所要求的路徑，並證實控制器確實為具有壓縮外擾的控制器。

With the growth of industrial development, the manufacturers change their manufacturing process to the production type of automatic manufacturing, so that they are able to promote their efficiency in production and to improve the quality of their products. In automatic manufacturing process, robot manipulators play essential roles and do the dangerous and repeated works with high precision in quality to cut down the production costs. Robot manipulators usually contain high nonlinearity in their dynamics and they are also classified as multi-input multi-output (MIMO) systems. Besides, based on the fact that robots are adopted in various environments, the rejection of external disturbances and parameter variation in the system becomes an important issue, which will be discussed in this research. Moreover, the external disturbances and parameter perturbation or uncertainties would lead to decrease the system performance or make the system become unstable. A hybrid adaptive-H∞ control, which is composed of the parameter-adaptive law and the control law, is proposed in this research to solve the disturbance and parameter-variation problems in robot manipulators. A two-link robot manipulator is adopted in computer simulation to attest the feasibility of the proposed adaptive-H∞ controller. The simulation results show that the tracing errors will asymptotically converge to zero in the presence of various system uncertainties. The simulation results also show that under the appropriate parameters chosen in this thesis, the system can effectively reject the plant uncertainties to achieve good tracking performance.

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