Zero-error tracking of iterative learning control using probabilistically quantized measurements

This paper considers the iterative learning control (ILC) problem using quantized measurements, which is generated by a probabilistic quantizer so that the introduced quantization error is a random variable with zero-mean and finite variance. The mean square and almost sure convergence is strictly established for the proposed algorithm. The results are then extended to the multi-sensor case where the variance of the averaged quantization error is reduced. A numerical example is provided to verify the results.