Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels.

The convergence performance of wireless networked iterative learning control systems is affected by additive noises, random delays, and data dropouts in both sensor‐to‐controller and controller‐to‐actuator channels. In order to guarantee the convergence performance of such systems, an input filter is designed in front of actuators to estimate the controller updated input under the effect of those uncertainties. Specifically, a P‐type learning controller is considered firstly, and then a model is developed to describe the transmission of sensor measured output data and controller updated input data under the mixed effect of those uncertainties. On the basis of state augmentation, the two developed data transmission processes are further combined with the controller learning process to build a unified filtering model. According to this filtering model and the projection theory, an optimal filter in linear minimum variance sense is designed at the actuator side to estimate the controller updated input in iteration domain. The convergence performance of the norm of filtering error covariance matrix is proved theoretically, which means driven by this accurately estimated input, the convergence performance of networked systems adopting the P‐type learning controller is guaranteed. Finally, some numerical results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]