Iterative learning control for networked stochastic systems with random packet losses

The iterative learning control is constructed for discrete-time stochastic systems with random measurement losses modelled by a stochastic sequence. A simple P-type update law is used and the almost sure convergence is strictly proved for both linear case and nonlinear case based on stochastic approximation. Illustrative examples show the effectiveness of the proposed approach.

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