Intermittent Sampling in Iterative Learning Control: a Monotonically-Convergent Gradient-Descent Approach with Application to Time Stamping

The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., in systems with data dropouts or when exploiting time-stamped data from incremental encoders. The aim of this paper is to develop a computationally tractable ILC framework for systems with arbitrary time- varying measurement points. New conditions for monotonic convergence of the input signal are established. These lead to a new single centralized design approach independent of the sampling times reminiscent of gradient-descent ILC. The approach is demonstrated in a simulation example of a massspring-damper system from which exact time-varying time- stamped data from the incremental encoder is available.