In wireless networked iterative learning control systems, the controller
is separated from the plant, and additive noises, random delays and data
dropouts arise in both sensor-to-controller and controller-to-actuator
channels. In order to guarantee the convergence performance of such
systems with the effect of these uncertainties, an input filter is
designed based on a proportional iterative learning controller, so that
updated inputs can be filtered at the actuator side. Specifically, two
data transmission processes are first developed to describe the mix of
those uncertainties in both channels by Bernoulli and Gaussian
distributed variables with known distributions. Based on state
augmentation, the two data transmission processes are further combined
with the iterative learning process of controllers to build a unified
filtering model. According to this unified model, an optimal filter is
designed via the projection theory and implemented at the actuator side
to filter the updated inputs in iteration domain. Moreover, the
convergence performance of the filtering error covariance matrix is
proved theoretically. Finally, some numerical results are given to
illustrate the effectiveness of the proposed method.
Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels
