Noisy Error-Adaptive Weighting Strategy for Accelerating ILC in Discrete-Time Systems

This article proposes a strategy to accelerate the convergence of iterative learning control (ILC) while maintaining robustness against stochastic noise. The strategy adaptively reweights the error signals used in conventional ILC schemes, casting greater influence to larger errors during input updates, thereby accelerating the correction of noisy inputs and improving overall convergence behavior. Furthermore, to mitigate the impact of noise-dominated small errors on weight computation, a saturation mechanism is introduced. A convergence theorem is established to characterize how the saturation parameters affect the asymptotic convergence of the input deviation-induced errors. Simulation and experimental results demonstrate that incorporating this strategy consistently improves convergence speed while maintaining tracking accuracy across different ILC implementations.