一個適用線性分散式系統之數位再設計自適應控制器且允許出使狀態不一致的重複學習演算法則在本論文中被提出，以用來改善系統暫態響應。給定包含了數個多輸入多輸出子系統的大型系統，以進化演算法來設計重複學習控制器以建構一個解耦參考模型，而此經過設計的參考系統可以去追蹤任何無法用數學形式來表示的軌跡，且無論在暫態或穩態都有良好的效果。接著，分散式自適應控制之數位在設計在本論文中被提出，因此大尺度取樣系統其狀態和軌跡可以和參考模型的狀態和軌跡軌跡儘可能的相近，且具備閉迴路解耦的特性。在設計的過程中，我們將數位再設計所得到的輸入當作重複學習演算控制的第一代輸入，以加速學習演算的速度並提昇整體的追蹤效果；除此之外，進化演算法用來搜尋重複學習演算的最佳增益，使的整個過程能夠有更佳的收斂效果。

In this thesis, a novel algorithm for digital redesign of the decentralized adaptive tracker for sampled-data linear large-scale system using evolutionary programming based iterative learning control with variation of initial state errors is proposed to improve the dynamic performance of transient responses. Based on the given sampled-data large scale linear system consisting of interconnected multi-input multi-output subsystems, the evolutionary-programming (EP) based iterative learning tracker is proposed to construct a decoupled well-designed reference model, so that the output response of the well-designed reference model will well track any trajectory (at both transient and steady state) specified at sampling instants, which may not be presented by the analytic reference model initially. Then, the 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-designed 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 any trajectory (both at transient and steady state), which may not be presented by the analytic reference model. During the process, a well-designed control input for the first generation of the iterative learning control (ILC) is constructed by applying the digital-redesign linear quadratic tracker to speed up the performance of the learning process. Besides, the EP is applied to tune the learning gain to further improve the ILC strategy.

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