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Indexed by:会议论文

Date of Publication:2016-05-28

Included Journals:EI、CPCI-S、SCIE、Scopus

Page Number:5790-5795

Key Words:Industrial batch process; input delay; time-varying uncertainties; iterative learning control (ILC); state predictor; robust H infinity control

Abstract:A robust closed-loop iterative learning control (ILC) method is proposed in this paper for industrial batch processes with input delay subject to time-varying uncertainties. By introducing a novel two-dimensional (2D) state observer for predicting the augmented closed-loop 2D system states to describe batch operation characteristics, only measured outputs of current and previous cycles are used for robust feedback control and ILC design. Delay-dependent sufficient condition in terms of matrix inequalities is established by constructing a comprehensive 2D Lyapunov-Krasovskii functional candidate along with free-weighting matrices. By solving these matrix inequalities using a modified cone complementarity linearization (CCL) method, the closed-loop ILC controller is explicitly formulated together with an adjustable robust H infinity performance index. An illustrative example of injection molding machine is shown to demonstrate the effectiveness and merit of the proposed ILC method.