Two-Side Data Dropout for Linear Stochastic Systems

Chapter

Abstract

This chapter contributes to the convergence analysis of ILC for linear stochastic systems under general data dropout environments, i.e., data dropouts occurring 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 instant is updated independently, which allows successive data dropouts along both time and iteration axes. The update mechanisms for both the computed input and real input are proposed and then the update process of both inputs is shown to be a Markov chain. By virtue of Markov modeling, a new analysis method is developed to prove the convergence in both mean square and almost sure senses.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

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