Effect of output noise in inverse-model-based iterative learning control

Inverse-model-based iterative learning control (ILC) for linear-time invariant, single-input single output (SISO) systems subject to output noise is proposed with the intent of predicting expectation of the underlying “noise-free” mean square error (Euclidean norm) on each iteration. Frequency domain formulae are derived to provide an insight into links between plant characteristics, noise spectra and inverse-model-based ILC parameters. Simulations are used to illustrate the theoretical findings.