Noisy Error-Adaptive Weighting Strategy for Accelerating ILC in Discrete-Time Systems
提出一种自适应加权策略，通过放大较大误差的影响并引入饱和机制抑制噪声，在保持鲁棒性的同时加速迭代学习控制的收敛速度。
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.