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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S、Scopus

Page Number:3587-3592

Key Words:Batch processes; input delay; two-degree-of-freedom control (2DOF); internal model control (IMC); iterative learning control (ILC); convergence

Abstract:In this paper, an indirect type iterative learning control (ILC) scheme is proposed for industrial batch processes with input delay, based on using a two-degree-of-freedom control (2DOF) internal model control (IMC) structure. An important merit is that the 2DOF IMC design for robust closed-loop control against process uncertainties and load disturbance is independent of the ILC design specifically for set-point tracking. To realize prefect tracking, an ILC controller in the form of proportional-derivative (PD) is proposed for the convenience of implementation. There is a single adjustable parameter in the proposed ILC controller, which may be monotonically tuned to meet a good trade-off between the tracking performance and its robustness against process uncertainties. Sufficient conditions to guarantee the convergence of ILC are derived. An illustrative example is used to demonstrate the effectiveness and merit of the proposed ILC method.