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In this Chapter, a predictive stateObserverobserver (PSO) based ILCIterative Learning Control (ILC) scheme is proposed for industrial batch manufacturing processes with delay responseDelay response suffering from nonrepetitive uncertainties and disturbances. Combining with a delay-freeDelay-freeoutput predictor, aPredictive State Observer (PSO)PSO-based feedback controlFeedback control structure is firstly presented to improve set-point trackingSet-point trackingand disturbance rejectionDisturbance rejectionperformance against nonrepetitiveProcess uncertaintiesprocess uncertaintiesNonrepetitive process uncertainty and disturbances for the initial batch runBatch run. Then, an ILC law is introduced to update the closed-loop systemClosed-loop system set-point in order to improve the tracking performanceTracking performance from batch to batch. A delay-independent sufficient condition is established to ensure the convergenceConvergenceof the outputTracking errortracking errorOutput tracking error along the batch direction. Moreover, another delay-dependent sufficient condition is constructed by LMILinear Matrix Inequality (LMI) to assess robust stabilityRobust stability of the proposed 2D control system along both the time and batch directions. An illustrative example and a real application to the batch temperature regulation of a 4-L crystallizer are shown to validate the effect and advantage of the proposed ILCIterative Learning Control (ILC) scheme.

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Predictive State Observer (PSO) Based ILC Design Under Output 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 predictive stateObserverobserver (PSO) based ILCIterative Learning Control (ILC) scheme is proposed for industrial batch manufacturing processes with delay responseDelay response suffering from nonrepetitive uncertainties and disturbances. Combining with a delay-freeDelay-freeoutput predictor, aPredictive State Observer (PSO)PSO-based feedback controlFeedback control structure is firstly presented to improve set-point trackingSet-point trackingand disturbance rejectionDisturbance rejectionperformance against nonrepetitiveProcess uncertaintiesprocess uncertaintiesNonrepetitive process uncertainty and disturbances for the initial batch runBatch run. Then, an ILC law is introduced to update the closed-loop systemClosed-loop system set-point in order to improve the tracking performanceTracking performance from batch to batch. A delay-independent sufficient condition is established to ensure the convergenceConvergenceof the outputTracking errortracking errorOutput tracking error along the batch direction. Moreover, another delay-dependent sufficient condition is constructed by LMILinear Matrix Inequality (LMI) to assess robust stabilityRobust stability of the proposed 2D control system along both the time and batch directions. An illustrative example and a real application to the batch temperature regulation of a 4-L crystallizer are shown to validate the effect and advantage of the proposed ILCIterative Learning Control (ILC) scheme.