A novel repetitive iterative learning control for linear discrete-time systems with time-iteration-varying reference

The purpose of this work is to improve the tracking performance of the iterative learning control (ILC) by designing a new learning law that has the ability to update the input along both the time and iterative axes. First, the reference is generated by a high-order internal model (HOIM) along the iterative axis and can be approximated by an HOIM along the time axis. Then, the HOIM-based repetitive control (RC) and ILC design methods are introduced, which can update the input along the time and iterative axes, respectively. Inspired by the design methods of the HOIM-based RC and ILC, a new ILC scheme, named as repetitive iterative learning control (RILC), is constructed by incorporating both the HOIMs of the reference along the time and iterative axes. Due to the additional use of the time-varying information of the reference, it is verified that the RILC is superior to the ILC. Finally, a microscale robotic deposition system is given to illustrate the advantage of the proposed RILC scheme.