摘要：Norm-optimal ILC enables high performance for systems that execute repeating tasks. Lifting techniques provide an analytic expression for the optimal feedforward signal. However, for large tasks the computational load increases rapidly for increasing task length. The aim of this paper is to show the benefits of a Riccati-based approach, which is developed in this paper for a general performance criterion and is applicable to both linear time-invariant (LTI) and linear time-varying (LTV) systems. The approach is implemented on an industrial position-dependent flatbed printer with large tasks which cannot be implemented using lifted ILC. Compared to lifted ILC, the proposed resource-efficient ILC provides the same high performance, but at a significantly smaller computational load (O(N) vs O(N 3)) making it more suitable for practical implementation.
关键词：Iterative learning controlresource-efficient ILCmotion controlfeedforward control designindustrial application
