A Novel Successive Updating Scheme of Iterative Learning Control for Networked Control System with Output Data Dropouts

This work investigates the problem of random successive data dropout at the output side of stochastic linear systems and presents a novel successive updating scheme (SUS) based on iterative learning control (ILC) to avoid control failures due to data loss. In particular, the successively lost output data in the latest iteration is compensated via predictive information estimated successfully with the same time instant label in the previous iteration by the multi-step predictive model. Mathematical induction is used to demonstrate the convergence of the proposed ILC scheme. Lastly, a simulation example is provided to back up the theoretical analysis.