本論文提出針對資料取樣的非線性大尺度系統包含數個多輸入多輸出非線性子系統以觀測器為基底的分散式自適應性軌跡追蹤器之數位再設計，並使用進化論演算法改進不好的系統條件的追蹤效能。基於已知的資料取樣的非線性大尺度系統包含數個多輸入多輸出非線性子系統，利用兩階段的分散式設計法設計一個解耦的參考模型，模型的輸出響應能夠追蹤到任何軌跡，即使軌跡在一開始是無法用數學函數表是或者在一個極大範圍內是沒有邊界限制。之後，另一個經過數位再設計的分散式自適應軌跡追蹤器被提出，此追蹤器能使一個被數位控制的資料取樣大尺度系統能夠具有經過良好設計的閉迴路參考模型一樣的解耦特性。因此，數位控制的資料取樣大尺度系統的輸出能夠追蹤設計的軌跡。當系統的狀態是不可量測時，一個以觀測器為基底的分散式自適應軌跡追蹤器被提出。此外，進化論演算法被提出用來針對不良條件的系統改進其狀態估測和追蹤效能。最後使用例子來證明本論文所提出的理論是有效的。

In this thesis, a novel digital redesign of the observer-based decentralized adaptive tracker for sampled-data nonlinear large-scale system consisting of nonlinear multi-input multi-output subsystems, using evolutionary programming to further improve the tracking performance for ill-condition systems, is proposed. Based on the given sampled-data large scale nonlinear system consisting of nonlinear multi-input multi-output interconnected subsystems, the decentralized two-stage design is proposed to construct a decoupled well-design reference model, so that the output response of the well-design reference model will well track any trajectory specified at sampling instant, which may not be presented by the analytic reference model initially, and it may not be bounded in a quite large range. Then, the other digital-redesign decentralized adaptive tracker is proposed, so that states of the digitally controlled sampled-data large-scale system closely match the ones of the well-design reference model with the closed-loop decoupling property. As a result, it yields the output of the digitally controlled sampled-data large scale system tracks well the trajectory, which may not be presented by the analytic reference model initially. When the state of the system is not measurable, an observer-based decentralized adaptive tracker is proposed. Besides, the evolutionary programming (EP) is applied to tune the observer gain to further improve the state estimation and tracking performance for the ill-conditional system. Finally, illustrative examples are given to demonstrate the effectiveness of the proposed methodology.

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