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论文类型：期刊论文

发表时间：2020-02-01

发表刊物：AUTOMATICA

收录刊物：EI、SCIE

卷号：112

ISSN号：0005-1098

关键字：Batch process; Time- and batch-varying uncertainties; Iterative learning control (ILC); Generalized extended state observer (GESO)

摘要：In this paper, a set-point related indirect-type iterative learning control (ILC) design is proposed for industrial batch processes with time-varying uncertainties and external disturbances. Different from the existing robust feedforward ILC methods solely focusing on error convergence along the batch direction, the proposed design has a double-loop control structure to conduct also dynamic control performance in the time direction as required in many engineering applications, where the inner loop is a generalized extended state observer based feedback control structure designed to ensure set-point tracking with robust stability in the time direction, and the outer loop consists of a simple proportional-type learning controller to update only the set-point command such that the tracking performance can be gradually improved along the batch direction. A tractable linear matrix inequality based sufficient condition is established to simultaneously guarantee bounded output tracking error and system input against time- and batch-varying uncertainties. An industrial injection molding process model is used to demonstrate the effectiveness and advantages of the new design in comparison to the recently developed robust feedforward ILC and indirect-type ILC designs. (C) 2019 Elsevier Ltd. All rights reserved.