Iterative learning control (ILC) enables high performance for exactly repeating tasks in motion systems. Besides such tasks, many motion systems also exhibit varying tasks. In such cases, ILC algorithms are known to deteriorate performance. An example is given by bonding equipment in semiconductor assembly processes, which contains motion axes with tasks that can vary slightly. The aim of this paper is to develop an ILC approach that obtains high machine performance for possibly varying tasks, while enabling straightforward and effective industrial design rules. In particular, a frequency-domain based design of ILC filters is pursued, which is combined with basis functions to cope with variations in tasks. Application to a highspeed axis of an industrial wire-bonder shows that high servo performance is obtained for both repeating and varying tasks.