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In this Chapter, a robust closed-loopIterative Learning Control (ILC)ILCClosed-loop ILC scheme is proposed for batch processesBatch processwith state delayState delayand time-varyingTime-varying uncertainties, based on a 2D system description of batch runBatch run. An important merit is that the proposed ILCIterative Learning Control (ILC) method can be used for on-line optimizationOn-line optimization against batch-to-batch process uncertaintiesProcess uncertainties to realize robust trackingRobust tracking of the set-point trajectoryTrajectory in both the time (during a cycle) and batchwise (from cycle to cycle) directions. Only measured output errors of current and previous cycles are used to design a synthetic ILC controller consisting of dynamicOutput feedbackoutput feedbackDynamic output feedbackplus feedforward controlFeedforward control, for the convenience of implementation. By introducing a comprehensive 2D difference Lyapunov function that can lead to monotonical state energy decrease in both the time and batchwise directions, sufficient conditions are established in terms of LMILinear Matrix Inequality (LMI) constraints for holding robust stabilityRobust stabilityof the closed-loopIterative Learning Control (ILC)ILCClosed-loop ILC system. By solving these LMI constraints, the ILC controller is explicitly formulated, together with an adjustable robust H-infinity performanceH infinity performance level. An illustrative example of injection moldingInjection molding is used to demonstrate the effectiveness and merits of the proposed ILCIterative Learning Control (ILC) method.

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Robust ILC Design Under Process State Delay

  • 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 robust closed-loopIterative Learning Control (ILC)ILCClosed-loop ILC scheme is proposed for batch processesBatch processwith state delayState delayand time-varyingTime-varying uncertainties, based on a 2D system description of batch runBatch run. An important merit is that the proposed ILCIterative Learning Control (ILC) method can be used for on-line optimizationOn-line optimization against batch-to-batch process uncertaintiesProcess uncertainties to realize robust trackingRobust tracking of the set-point trajectoryTrajectory in both the time (during a cycle) and batchwise (from cycle to cycle) directions. Only measured output errors of current and previous cycles are used to design a synthetic ILC controller consisting of dynamicOutput feedbackoutput feedbackDynamic output feedbackplus feedforward controlFeedforward control, for the convenience of implementation. By introducing a comprehensive 2D difference Lyapunov function that can lead to monotonical state energy decrease in both the time and batchwise directions, sufficient conditions are established in terms of LMILinear Matrix Inequality (LMI) constraints for holding robust stabilityRobust stabilityof the closed-loopIterative Learning Control (ILC)ILCClosed-loop ILC system. By solving these LMI constraints, the ILC controller is explicitly formulated, together with an adjustable robust H-infinity performanceH infinity performance level. An illustrative example of injection moldingInjection molding is used to demonstrate the effectiveness and merits of the proposed ILCIterative Learning Control (ILC) method.