The iterative learning control (ILC) problem is addressed in this paper for stochastic nonlinear systems with random data dropouts. The data dropout is modeled by the conventional Bernoulli random variable to describe the successful transmission or loss. Both intermittent and successive ILC are considered, where the former stops updating if no information is received, while the latter keeps updating based on the latest available data. It is strictly proved the almost sure convergence of both algorithms. The simulations on a mechanical model are provided to show the comparisons and effectiveness of the proposed algorithms.
An iterative learning control algorithm using quantized error information is proposed in this article for both linear and nonlinear systems. The actual output is first compared with the reference signal and then the corresponding error is quantized and transmitted. A logarithmic quantizer is used to guarantee an adaptive improvement for tracking performance. The tracking error under this scheme is proved to converge to zero asymptotically. Illustrative examples verify the theoretical results.
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