Iterative Learning Control: Brief Survey and Categorization

By Hyo-Sung Ahn, YangQuan Chen, Kevin L. Moore. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2007

1,550 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning (2021, background): “Previous review works focused on speciﬁc techniques—for example, learning-based model predictive control (MPC) (7), iterative learning control (ILC) (8, 9), model-based RL (10), data-eﬃcient policy search (11), imitation learning (12), or the use of RL in robotics (13, 14) and in optimal control (15”
From model-based control to data-driven control: Survey, classification and perspective (2013, background): “In addition, ILC has been widely applied in many fields [3,59].”
Data-Based Techniques Focused on Modern Industry: An Overview (2015, background): “Due to the excellent characteristic on computational load, ILC-related approaches have been intensively discussed [60], and the effectiveness has been approved in numerous literature,”
Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties (2014, methodology): “As surveyed by Bonvin et al [5], Ahn et al [6], and Wang et al [7], most of existing references have been devoted to time- invar ia t linear or nonlinear batch processes.”
Experimentally verified generalized KYP Lemma based iterative learning control design (2016, methodology): “, the survey papers (Ahn et al., 2007; Bristow et al., 2006; Wang et al., 2009), where the last of these has a particular focus on run-to-run control as found in the chemical process industries.”