Analysis and comparison of iterative learning control schemes

Abstract

Iterative learning control (ILC) schemes can be classified into the previous cycle learning (PCL), the current cycle learning (CCL) and the synergy—previous and current cycle learning (PCCL). In this work, we first present the configurations of various ILC schemes and the corresponding convergence conditions associated with each configuration. As a result of comparison, the PCCL scheme shows the ability of outperforming the PCL and CCL schemes owing to its underlying feature of two degrees of freedom design. Subsequently, we focus on two practical PCCL schemes with analysis and comparisons in frequency domain, substantiate the difference in the learning updating mechanisms, and in the sequel exploit the circumstances where one PCCL scheme can outperform the other. Based on system Bode plots, we can easily check the learning convergence condition, the complementary property of feedback and feedforward compensation, and which PCCL scheme can perform better. For the purpose of comparison and verification, both schemes are applied to a real-time ball-and-beam system.