Input Estimation for Repetitive-Motion Plants via ILC Subject to Quantization Errors and Packet Dropouts

Quantization errors and packet dropouts in noisy communication channels degrade the convergence performance of repetitive-motion plants under iterative learning control (ILC). Different from existing works focusing on the redesign of ILC schemes, this paper regards the ILC-generated input as the iteration-domain state of the learning process, and proposes an estimation approach to reconstruct the actual input. Provided that the basic ILC scheme achieves convergence under ideal communication conditions free of quantization and packet dropouts, the proposed estimator mitigates the plant-side input deviation via input reconstruction in the presence of such disturbances in input–output channels. Simulation results illustrate the potential of the reconstructed input to improve the tracking performance of the controlled plant under such channel disturbances.