Abstract
For industrial batch processes with time-varying uncertainties, a robust output feedback based iterative learning control (ILC) design is proposed by introducing finite frequency specifications, based on only the output measurement. The integral of output tracking error in the time domain is incorporated into the ILC updating law to improve the tracking performance from batch to batch. Based on the generalized Kalman-Yakubovich-Popov lemma, robust stability of the resulting closed-loop ILC system along both time and batch directions is analyzed in the repetitive system framework. The corresponding ILC law can be determined from the established tractable linear matrix inequality conditions. Moreover, it is shown that the obtained stability condition can also guarantee the bounded output tracking error and system input in the presence of time- and/or batch-varying uncertainties. An illustrative example from the literature is adopted to demonstrate the effectiveness of the proposed design.
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