Iterative learning control for non-normal and biased measured targets

Abstract

In the original iterative learning control (ILC) algorithm, it is commonly assumed that the target signal remains constant throughout iterations. However, this assumption may not be satisfied in practical industrial applications. Therefore, this paper proposes a novel ILC approach for non-normal and biased measured targets, in which the target is not predetermined by a fixed curve or formula but generated from the generation system. The iterative learning control problem is first formulated, followed by algorithm implementation through mechanism analysis, process determination, and assessments for feasibility and convergence. The proposed algorithm is simulated subsequently. Results demonstrate that application of this algorithm can effectively minimize expected error between non-normal and biased measured targets and output. After a sufficient number of iterations, the tracking error will originate solely from the trajectory itself.