Impact of Measurement Noise on Networked Iterative Learning Control Systems

The paper studies the impact of measurement noise on the tracking performance of networked iterative learning process for a class of discrete-time systems with random packet dropout. It is strictly proved that the expectation of the tracking error of the iterative learning process is bounded and its upper bound is determined by the standard deviation of the measurement noise and the initial error of control input. And an example is given to show that the result obtained is effective.