An Automatic Method for Selecting Convergence Ratios in Iterative Learning Control

Iterative learning control (ILC) is a technique for improving machining accuracy where the parameter convergence ratio significantly affects the learning efficiency. This paper reports our development of a process for two-axis machining that automatically generates a suitable convergence ratio with each learning iteration. This algorithm not only quickly decreases the root mean square contour errors, but also reduces the total learning iterations required for machining. An additional algorithm is developed to translate a G-code file to the corresponding path equations, facilitating automation of the whole process of ILC machining. The proposed algorithms are implemented and integrated into LinuxCNC. Each machining process for the tested machining paths could be completed automatically. Experimental results show that using the convergence ratios generated by the proposed algorithm can help improve machining accuracy quickly, and in fewer learning iterations compared to set the ratios manually by experiences. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.