Higher Order Iterative Learning Control of Discrete Linear Systems with Uncertain Parameters

Iterative learning control emerged from the problem area of increasing the accuracy of finite-duration repetitive operations performed by robots, often termed trials. The ILC laws use past trial information to adjust the current trial's control signal. Most often, only data from the previous trial is used. A higher-order ILC law uses information from several previous trials. Recently, interest in these laws has increased in the literature with, in particular, robotic additive manufacturing problems. This paper develops a new higher-order ILC design for discrete linear uncertain systems that makes greater use of information generated over previous trials. An example using a model developed from measured frequency response data from a laboratory testbed illustrates the new design.