Networked Iterative Learning Control for Linear-time-invariant Systems with Random Packet Losses

This paper develops two proportional-type networked iterative learning control (NILC) schemes for a class of linear-time-invariant systems with stochastic packet dropout being subject to Bernoulli-type distribution. In the NILC schemes, we consider two types of compensation algorithms for dropped data: one of which is to replace the dropped data by that of the successfully captured at the concurrent sampling moment of the latest iteration, and the other is to utilize the desired output at the concurrent sampling moment to compensate for the missed data. In terms of the proposed NILC schemes, sufficient conditions for convergence are derived in the sense of expectation. Numerical experiments illustrate the effectiveness of the NILC schemes.