  Growing demands in today's industry results in increasingly stringent
performance and throughput specifications. For accurate positioning of
high-precision motion systems, feedforward control plays a crucial role.
Nonetheless, conventional model-based feedforward approaches are no longer
sufficient to satisfy the challenging performance requirements. An attractive
method for systems with repetitive motion tasks is iterative learning control
(ILC) due to its superior performance. However, for systems with non-repetitive
motion tasks, ILC is {generally} not applicable, {despite of some recent
promising advances}. In this paper, we aim to explore the use of deep learning
to address the task flexibility constraint of ILC. For this purpose, a novel
Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To
benchmark the performance of the proposed approach, a simulation study is
presented which compares the TAIL-ILC to classical model-based feedforward
strategies and existing learning-based approaches, such as neural network based
feedforward learning.

Systems and Control;Systems and Control