Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

• Published in: Chinese journal of chemical engineering Vol. 17; no. 6; pp. 976 - 982
• Main Author: 周猛飞 王树青 金晓明 张泉灵
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2009
• Subjects: continuous/batch process; event monitoring; iterative learning; model predictive control; soft constraint; 作业过程; 周期性扰动; 技术应用; 控制功能; 模型预测控制; 管理控制器; 迭代学习算法; 间歇过程; model predictive control; event monitoring; iterative learning; continuous/batch process; soft constraint
• ISSN: 1004-9541; 2210-321X
• DOI: 10.1016/S1004-9541(08)60305-5

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.