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

一个反复的学习模型预兆的控制(ILMPC ) 技术被用于 continuous/batch 过程的一个班。如此的过程被产生周期的强壮的骚乱到连续过程的批过程的操作描绘，传统的规章的控制器是不能的消除这些周期的骚乱。ILMPC 集成处理重复信号和灵活性的反复的学习控制(ILC ) 的特征为预兆的控制(MPC ) 建模。由联机监视，批的操作地位处理，事件驱动为批的反复的学习算法重复骚乱被开始，软限制被调整象可行区域及时离开需要的操作地区。一个工业应用程序的结果证明建议 ILMPC 方法为 continuous/batch 进程的一个类是有效的。

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.