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In this chapter, a set-point learning based indirect-typeIterative Learning Control (ILC)ILCIndirect-type ILC design is proposed for industrial batch processesBatch processwith time-varyingTime-varying uncertainties and external disturbancesExternal disturbance. Different from the existing robust feedforward ILCIterative Learning Control (ILC) methods solely focusing on error convergenceConvergence 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 loopInner loop is a generalized extended stateObserverobserverExtended Sate Observer (ESO) (ESO) based feedback controlFeedback control structure designed to ensure set-point trackingSet-point trackingwith robust stabilityRobust stability in the time direction, and the outer loopOuter loop consists of a simple proportional-type learning controller to update only the set-point commandSet-point command such that the tracking performanceTracking performance can be gradually improved along the batch direction. A tractable LMILinear Matrix Inequality (LMI)-based sufficient condition is established to simultaneously guarantee bounded outputTracking errortracking errorOutput tracking error and bounded system input under time- and batch-varyingBatch-varying uncertainties. An injection moldingInjection molding process model is used to verify the effectiveness and advantages of the proposed design.

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Extended State Observer (ESO) Based ILC Design Under Process Uncertainties and Disturbance

  • 章节

  • 发表日期：

    2026年

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  • Tao Liu,
  • Shoulin Hao,
  • Youqing Wang,
  • Dewei Li

摘要

In this chapter, a set-point learning based indirect-typeIterative Learning Control (ILC)ILCIndirect-type ILC design is proposed for industrial batch processesBatch processwith time-varyingTime-varying uncertainties and external disturbancesExternal disturbance. Different from the existing robust feedforward ILCIterative Learning Control (ILC) methods solely focusing on error convergenceConvergence 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 loopInner loop is a generalized extended stateObserverobserverExtended Sate Observer (ESO) (ESO) based feedback controlFeedback control structure designed to ensure set-point trackingSet-point trackingwith robust stabilityRobust stability in the time direction, and the outer loopOuter loop consists of a simple proportional-type learning controller to update only the set-point commandSet-point command such that the tracking performanceTracking performance can be gradually improved along the batch direction. A tractable LMILinear Matrix Inequality (LMI)-based sufficient condition is established to simultaneously guarantee bounded outputTracking errortracking errorOutput tracking error and bounded system input under time- and batch-varyingBatch-varying uncertainties. An injection moldingInjection molding process model is used to verify the effectiveness and advantages of the proposed design.