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This technical note contributes to the convergence analysis for iterative learning control (ILC) for linear stochastic systems under general data dropout environments, i.e., data dropouts occur randomly at both the measurement and actuator sides. Data updating in the memory array is arranged in such a way that data at every time instance is updated independently, which allows successive data dropouts...
This paper contributes to the convergence analysis of iterative learning control (ILC) for a linear time-varying system with measurement data dropouts, where the data dropout problem is formulated by a Markov chain model. The widely used Bernoulli model for data dropout is a special case of the Markov chain model. A regulating parameter is added to the classic P-type update law. The mean square and...
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