Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

为在批的产品质量的追踪的控制的最佳的反复的学习控制(ILC ) 策略处理的 batch-to-batch 被介绍。线性变化时间的不安(LTVP ) 模型在名字的轨道附近为产品质量被造。到模型植物失配的地址问题，在以前的成批处理的模型预言错误为当前的成批处理被加到模型预言。然后追踪错误转变模型能被造，并且有直接错误反馈的 ILC 法律明确地被获得。一条严密定理被建议，到证明在 ILC 下面追踪错误的集中。建议方法论在一个典型的批反应堆和轨道追踪的表演被 ILC 逐渐地改进的结果表演上被说明。

A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained, A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.