模式預測控制不僅適用於製程之多變數最佳控制，且能針對操作或被控變數依實際需求設限。疊代學習控制適用於具重複操作特性之控制系統，能逐步消除模式不確定性和重複性擾動對製程的影響。目前已有學者提出兩種疊代學習模式預測控制，分別使用動態矩陣控制與狀態空間模式預測控制，可用來改善批次製程之控制性能，其缺點為僅以輸出誤差來評估改善效果，且未完整探討變數設限與時延的問題。
本文首先比較二次動態矩陣控制與傳統數位控制，證實前者針對SISO和MIMO系統皆能獲得較佳的控制，但也發現在製程含未知擾動或模式不吻合時，其控制性能仍具改善空間。針對批次製程對受限多變數最佳控制的需求，本文提出疊代學習二次動態矩陣控制(ILQDMC)，能夠逐批改善製程的控制性能，其改善標準為在含特定輸出入加權的目標函數不變差的情況下，輸出誤差平方和持續變好。ILQDMC提供第一步設限和全部設限兩種操作變數設限方式，且所使用的設計公式極適合疊代學習計算。與疊代學習狀態空間模式預測控制(ILSSMPC)的比較發現，當模式相同且不含時延時，ILSSMPC之控制性能一般優於ILQDMC；當模式含時延或不易取得時，建議採用ILQDMC。模擬研究顯示，第一步設限方式適合輸入權重設定值較小的SISO系統；而全部設限方式適合MIMO系統。此外，在輸入權重值不變時，疊代學習控制無法保證控制性能長久持續改善，因此當改善效果變差時，可藉由增加權重值來停止疊代學習，以維持原有的良好控制。

Iterative learning control is suited to a control system characterized by repetitive operation. It can gradually eliminate the influence caused by model uncertainty and repetitive disturbances. Some approaches of iterative learning model predictive control have been proposed to improve the control performance of a batch process. The shortcomings are that they evaluated the improvement only by virtue of the output errors and did not thoroughly investigate the problems of variable constraints and time delay.
In this thesis, quadratic dynamic matrix control is first compared with the traditional digital control to find that the former is better for both SISO and MIMO systems. However, it is also found that there is still room for further improvement if model mismatch or unknown disturbances exist in the process. Therefore, a method called iterative learning quadratic dynamic matrix control (ILQDMC) is proposed to provide constrained multivariable optimal control required by batch processes. ILQDMC can improve the control performance batch by batch in the sense that the sum of squared output errors gets better without sacrificing the objective function formed by weighted outputs and inputs. ILQDMC offers two ways to constrain manipulated variables, i.e., first step constraint and all steps constraint. The design formula are quite suitable for the computation of iterative learning. A comparison with iterative learning state-space model predictive control (ILSSMPC) reveals the following: the performance of ILSSMPC is generally better when employing the same model without time delay; ILQDMC should be adopted when the model possesses time delay or is hard to obtain. Simulation studies indicate that first step constraint is suited to SISO systems with smaller input weights, while all steps constraint works for MIMO systems. In addition, to maintain good control batch by batch, the input weight should be increased to stop iterative learning as the performance begins to become worse.

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