Sparse iterative learning control (SPILC): when to sample for resource-efficiency?

Iterative learning control enables the determination of optimal command inputs by learning from measured data of previous tasks. The aim of this paper is to address the negative impact of trial-varying disturbances that contaminate these measurements, both in terms of resource-efficient implementations and performance degradation. The proposed method is an optimal framework for ILC that enforces sparsity and related structure on the command signal. This is achieved through a convex relaxation relying on ℓ1 regularization. The approach is demonstrated on a benchmark motion system, confirming substantial extensions compared to earlier results