论文信息 - RECENT PROGRESS AND INDUSTRIAL EXPERIENCES WITH NONLINEAR MODEL IDENTIFICATION FOR MPC APPLICATIONS IN POLYMER MANUFACTURING

RECENT PROGRESS AND INDUSTRIAL EXPERIENCES WITH NONLINEAR MODEL IDENTIFICATION FOR MPC APPLICATIONS IN POLYMER MANUFACTURING

Abstract One of the most important areas for industrial Nonlinear Model Predictive Control (NMPC) applications has been polymer manufacturing. This brief paper presents recent progress and some of experiences in NMPC applications from industrial practitioners. Several aspects leading to a successful project are covered. Issues and a wish list are also provided for discussion and future research.

[1] J. Guiver,et al. State-space nonlinear process modeling : Identification and universality , 1998 .

[2] Hong Zhao,et al. An identification approach to nonlinear state space model for industrial multivariable model predictive control , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[3] Paul Turner,et al. Introducing the bounded derivative network—superceding the application of neural networks in control , 2005 .

[4] S. Joe Qin,et al. A survey of industrial model predictive control technology , 2003 .

[5] Robert E. Young,et al. Evolution of an Industrial Nonlinear Model Predictive Controller , 2002 .

[6] Hong Zhao,et al. A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model☆ , 1999 .

[7] Stephen Piche,et al. Nonlinear model predictive control using neural networks , 2000 .

[8] Paul Turner,et al. Experiences with Nonlinear MPC in Polymer Manufacturing , 2007 .