大腸桿菌（Escherichia coli）程序除了碳源基質（carbon source substrate）外，氧亦是重要的因子或必需基質（essential substrate）。對於碳源基質與溶氧同為生物生長必需基質之饋料批次程序而言，同時維持兩個必需基質濃度於最適（optimal）值方能達到最大生物產量之要求。然而，生化程序的狀態變數多數無法有效的線上量測，僅以開環最適化操作策略缺乏韌性，模式不確定性（uncertainty）引起的瓶頸效應將嚴重劣化產率。
本研究引用結合前饋（feedforward）與回饋（feedback）控制的多環路控制策略，前饋以開環最適化原則控制；回饋控制則經由文獻中動態相對增益陣列（dynamic relative gain array, DRGA）分析的結果，決定其適當之配對。對於生化程序之強烈非線性特性，回饋控制引用模糊建模（fuzzy modeling）所建立之多重模式（multimodel），配合IMC調諧方法，以增益排程（gain scheduling）的PID控制器即完成非線性控制，其目的在於消除前饋控制未預期之模式誤差等干擾。多重模式的建立僅需由程序的起始時間與終止時間的近似模式內插組合，此控制策略對於大腸桿菌生長模式的程序亦具有良好的控制成效並具有優越的韌性。
另外，臨界點所引發的碳源基質設定點錯誤的問題，本研究則利用大腸桿菌在臨界點時的特性，設計一簡單的邏輯以逼近之。透過pH值的響應，所設計之動態搜尋法可以有效的將醋酸濃度壓至最低並促使大腸桿菌的產率逼近最佳值。

For the process of Escherichia coli, dissolved oxygen in fed-batch fermenter is also an important factor or essential substrate besides the carbon source substrate. It is necessary to maintain the concentration of these two essential substrates at optimized operation to obtain maximum productivity. However, it is difficult to obtain most state variables using the method of online measurements. It’s less robust to utilize open-loop optimized operation. Bottlenecks effect due to model uncertainty will reduce the productivity significantly. Multiloop control strategy that combines a feedforward controller with a feedback controller is proposed in this study. The feedforward controller is an open-loop optimal controller, and the adequate pairs of the feedback controller are determined via the dynamic relative gain array (DRGA). For the nonlinearity of the bioprocess, a gain scheduling feedback controller is employed by a multimodel approach with fuzzy modeling, and is tuned using Internal Model Control （IMC） principle to reject loads which is unpredictable in the feedforward controller. The structure of the multimodel utilizes interpolation techniques using only two local models at initial and terminative operating times.
In all the model parameters, the uncertainty of critical point of the substrate uptake rate will give the wrong setpoint value of the control loop which maintains the carbon source substrate concentration. The behavior of E.coli at this critical point will be used to design a simple set of fuzzy logic rules so that the approaching of the critical point can be achieved. By the response of pH, the method can be used to inhibit the concentration of acetate and make the concentration of E.coli to approach the optimal concentration profile.

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