一般的化學程序多為非線性的系統，而不同的系統所表現的非線性程度有異，有些系統可以用線性方程來表示，反之，有些系統的非線性則完全無法被表示，同時，以一般的線性控制器並無法進行控制，因此有了以系統模式做為運算基礎所衍生出來的控制邏輯和策略，如模式預測控制(Model Predictive Control, MPC)，其整體控制表現與所採用的模式本身的預測準確度息息相關，對於一個高度非線性的化學反應系統，採用非線性模式，如人工智慧類神經網路，為基礎的控制方式顯然優於使用線性模式為控制基礎的策略。
在這一篇的論文裡，在PI控制器應用於高非線性的系統時，我們不採改變PI控制參數的方式，而是以一個預測控制器作為一個全局(global)的輔助控制器，以解決PI控制器在非線系統中僅能作局部(local)控制的問題，同時，我們亦用這個方法來改進以ANNs 為模式的MPC在模式不準確時的控制，在ANNs 建模時，常因訓練資料不完全或其它的因素，使得模式有誤差存在，而無法有效的控制，在這個架構裡，PI 可用來修正MPC的控制效果，另外，我們將這個控制策略應用在pH滴定中和系統做為測試，在實際的進行控制實驗的結果中顯示出，以類神經網路所建構的模式，能夠對系統在動態時的變化作有效的控制動作，配合PI控制器作系統穩態時的控制，能夠在非線性的系統有非常好的表現。

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