以數位控制器來實現傳統連續式順滑模態控制理論設計的控制律時，由於數位控制器在每個取樣週期所輸出的控制訊號為一定值，而取樣頻率亦需受到硬體限制，故系統即使在無外擾、無模型參數不確定性下，仍無法保證控制目標之達成，甚至會造成系統的不穩定。另外，離散順滑模態控制由於取樣時間的限制無法使其具有如同連續順滑模態控制之強健性，且易產生氈振現象，使得順滑控制的性能受到影響。本文利用反覆式學習控制法，由反覆操作受控系統輸出及輸入的動態即時資料來改善控制系統之行為並減少其氈振的現象發生，並希望控制的結果能夠隨著時間以及學習次數收斂，期能同時保留順滑模態的強健性，以及反覆式學習法的適應自調性，達到系統要求的控制目標。

The performance of the sliding mode controller realized by a digital computer will be limited due to the sampling frequency and the mechanism of sampling and hold process. Therefore, even without disturbance and uncertainties, the goal of the control system may not be achieved. In some cases it may result in system instability and chattering. On the other hand the robustness and invariance properties can not be guaranteed as the continuous time case. In this thesis Iterative Learning Control concept is introduced to improve the performance of discrete sliding mode control system. With the proposed algorithm, the resulting system performance can be improved and the chatting effect can be suppressed. The effectiveness of the learning process is illustrated by study cases.

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