การควบคุมเชิงทำนายแบบจำลองไม่เชิงเส้นวีเนอร์ที่ใช้ฟังก์ชันลาแกร์สำหรับหอกลั่นแยกสารสองชนิด - อนวัช ฮารุดีน (จุฬาลงกรณ์มหาวิทยาลัย, 2011)
This thesis presents Wiener nonlinear model predictive control with Laguerre function and application to binary distillation column. Wiener model composes of linear dynamic model and nonlinear function in cascade connection. Wiener model can explain the behavior of complex industrial process. Employing Wiener model in nonlinear model predictive control, we can retain the important properties of linear model predictive control, namely, the cost function has a form of quadratic function. However, the number of variables varies with control horizon or the number of control input. When control horizon is large, it causes a heavy computational burden and results in an ill condition number. In this thesis, we use the Laguerre function and exponential weighting to handle these problems. Applying Laguerre function to nonlinear model predictive control can reduce the number of variables used in the optimization problem. Moreover, employing exponential weighting solves the ill condition number and improves the reliability of solution. Lastly, we demonstrate the effectiveness of the proposed method with an application to binary distillation column. In the experimental result, we can reduce the number of variables in the optimization problem from 80 to 5 and improve the condition number from 2.803X10[superscript 6] to 407.36.