非線性程序可分為兩類，第一類程序的動態和靜態部份皆為非線性;第二類程序的動態部份為線性但靜態部份為非線性。針對第一類非線性程序，我們利用延伸線性化來取得參數化轉移函數，其參數可為程序穩態輸入或輸出。接著根據內部模式控制(IMC)來產生一非線性IMC控制器設計。在參數化控制器的實現上，我們提出以殘餘矩陣法來得到狀態空間表示式。高階液位槽系統的模擬結果顯示所提非線性IMC控制器在設定點改變的情況下具有比相應線性IMC控制器更好之動態響應。所提控制器設計法亦可配合由程序鑑別得到的參數化轉移函數模式，其效果與應用延伸線性化的設計相近。
　　對於第二類非線性程序，我們假設其可鑑別成Hammerstein模式，先以反函數來消除模式的靜態非線性部份，即可使用線性的IMC控制器設計。文中探討三種方式來得到靜態非線性部份的反函數，並對一非線性連續攪拌反應槽系統作控制器設計，結果顯示此設計在設定點改變時確能獲得不錯的效果。

　　Nonlinear processes can be classified into two types. The first type of processes consists of nonlinear static and dynamic parts, whereas the second type consists of a linear dynamic part and a nonlinear static part. For the first type of processes, we can obtain a parameterized transfer function via extended linearization, where the parameter represents the steady-state input or output of the process. We then design a nonlinear IMC (internal model control) controller based on the IMC theory. To realize the parameterized IMC controller, we propose to convert the transfer function representation of the controller into a state-space representation by means of the residual matrix approach. Simulation results with high-order level-tank systems reveal that the proposed nonlinear IMC controller outperforms the corresponding linear IMC controller for set-point changes. We have the third-order level-tank system model by process identification. The proposed IMC design can also be incorporated with a parameterized model provided by process identification. This is verified on a third-order level-tank process.
　　For the second type of processes, we assume that identification as a Hammerstein model is appropriate and provide an inverse function to cancel out the static nonlinearity of the model. A linear IMC controller can then be used to control the resulting system. In this thesis, three approaches are investigated to arrive at an inverse function of the static nonlinearity. Simulation with a continuous stirred tank reactor system reveals that the proposed method works well for set point changes.

[1] Bequette, B.W., “Nonlinear Predictive Control
of Chemical Process : A Review,” Ind. Eng.
Chem. Res., 30, 1391,1991.

[2] Billings, S.A. and W.S.F. Voon, “Structure
Detection and Model Validity Tests in the
Identification of Nonlinear Systems,” IEE
Proceedings, 30, 193, 1983.

[3] Boje, E., H.A. Spang and E.Gottzein,
“Application of Gain Scheduling,” IFAC
11th Triennnial World Congress, Tallinn, USSR,
1990.

[4] Curtis, F.G. and O.W. Patrick, “Applied
Numerical Analysis 5th,” Addison-Wesley
Publishing Company, 1994.

[5] Haber, R.,“Structure Identification of
Quadratic Block-Oriented Models Based
on Estimated Volterra Kernels,” Int. J. Syst.
Sci., 20, 1355, 1989.

[6] Haber, R. and H. Unbehauen, “Structure
Identification of Nonlinear Dynamics
Systems – A Survey on Input/Output
Approaches,” Automatica, 36, 654, 1990.

[7] Henson, M.A. and D.E. Seborg, “ Input-output
Linearization of General Nonlinear Process,”
AIChE. J. 36, 1753, 1990.

[8] Lin, C. F., Advanced Control System Design,
Prentice-Hall, NJ,1994.

[9] Menold, P.H., F. Allgöwer and P.K. Pearson,
“Nonlinear Structure Identification of
Chemical Engineeing,” Comput. Chem. Eng., 21,
137, 1997.

[10] Morari, M. and E. Zafiriou, Robust Process
Control, PrentinceHall:Englewood Cliffs, NJ,
1989.

[11] Ogunnaike, B.A. and W.H. Ray, Process Dynamics,
Modeling and Control, Oxford Inc, New York,
1994.

[12] Ray, W.H., Advanced Process Control,
McGraw-Hill Inc., 1981.

[13] Rugh, W.J.; “Design of Nonlinear PID
Controllers,” AIChE J., 33, 1738, 1987.

[14] Rugh, W.J., “Analytical Framework for Gain
Scheduling,” IEEE Contr. Syst. Mag., 11, 79,
1991.

[15] Shahruz, S. and S. Behtash, “Design of
Controllers for Linear Parameter Varying
System by Gain Scheduling Technique,” J. Math.
Anal. Appl., 168, 195, 1992.

[16] Shamma, J.S. and M. Athans, “Analysis of
Nonliear Gain Scheduled Control Systems,” IEEE
Trans. Automatic Control., AC-35, 898, 1990.

[17] Shamma, J.S. and M. Athans, “Gain Scheduling :
Potential Hazards and Possible Remedies,” IEEE
Control Systems, 12, 101, 1992.

[18] Sistu, P.B., “Nonlinear Predictive control of
Uncertain Processes:Application to a CSTR,”
AIChE J., 37, 1711, 1991.

[19] Wang, G.W., S.S. Peng, and H.P. Huang, “A
Sliding Observer for Nonlinear Process
Control,” Chem. Eng. Sci., 52, 787, 1997.

[20] Ziegler, J.G. and N.B. Nichols; “Optimum
Setting for Automatic Controllers,” Trans.
ASME, 64 , 759, 1942.
[21] Wang, L.W. (王力威) “穩定、積分及不穩定連續程
序之鑑別與控制,” 2003.
[22] Hwang, S. H. Private communications, 2004。