中文摘要：

在工业为一个批过程基于一个相等的二维的 Fornasini-Marchsini 模型，一个靠近环的柔韧的反复的学习差错容忍的保证费用控制计划与致动器失败为批过程被建议。这份报纸介绍差错容忍的保证费用控制的相关概念并且提出柔韧的反复的学习可靠保证费用控制器(ILRGCC ) 。一个重要优点是建议 ILRGCC 设计方法能对 batch-to-batch 过程无常被用于联机优化认识到及时的集合点轨道的柔韧的追踪， batch-to-batch 定序。为实现的便利，仅仅测量了电流的输出错误，以前的周期被用来为反复的学习控制设计一个合成控制器，由加前馈控制控制的动态输出反馈组成。建议控制器不能仅仅沿着时间和周期序列保证靠近环的集聚而且为所有可被考虑的无常和任何致动器失败与上面的界限满足 H 表演水平和费用功能。为控制器答案的足够的条件以线性矩阵不平等(LMI ) 被导出，并且设计过程，与 LMI 限制提出一个凸的优化问题，被介绍。注射塑造的一个例子被给说明有效性， ILRGCC 设计的优点来临。

英文摘要：

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller （ILRGCC）. A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities （LMIs）, and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.