Abstract
This chapter contributes to the convergence analysis of ILC for linear systems under general data dropouts at both measurement and actuator sides. By using a simple compensation mechanism for the dropped data, the sample path behavior of the input sequence along the iteration axis is analyzed and formulated as a Markov chain first. Based on the Markov chain, the recursion of the input error is reformulated as a switching system, and then a novel convergence proof is established in the almost sure sense under mild design conditions.
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Shen, D. (2018). Two-Side Data Dropout for Linear Deterministic Systems. In: Iterative Learning Control with Passive Incomplete Information. Springer, Singapore. https://doi.org/10.1007/978-981-10-8267-2_8
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DOI: https://doi.org/10.1007/978-981-10-8267-2_8
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