Iterative Learning Control of Mechanical Systems With Slow Sampling Rate Position Measurements

Abstract

This paper deals with the iterative learning controller design problem with slow sampling rate position measurements. In some applications, it is desired to implement the learning controller at a sampling rate higher than that of the measurement. Two approaches are presented in this paper to solve the learning controller design problem. The first approach utilizes an interlacing technique to obtain the slow sampling rate position measurement, and update the learning controller at the desired sampling rate using only the available slow sampling rate measurement. The second approach uses a multirate kinematic Kalman smoother to obtain the position estimate at the desired sampling rate by fusing the position measurement and additional acceleration measurements at fast sampling rates. The effectiveness of the two approaches is demonstrated by experiments.