A survey of iterative learning control

By Douglas A. Bristow, Marina Tharayil, Andrew G. Alleyne. IEEE Control Systems, 2006

2,844 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/144cc485649191364635bdc01b0a7e5a6b111ec3

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In Control and Systems Engineering

Citations in the corpus

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”
Iterative Learning Control for RF Power Amplifier Linearization (2016, background): “In reality, due to problems encountered in the identification process and limitations of the models used in the predistorter, residual distortions can be found at the output of the amplifier.”
Learning contact-rich manipulation skills with guided policy search (2015, background): “controllers can be viewed as accomplishing a similar task to iterative learning control (ILC), though ILC optimizes a controller for following a known trajectory [23], while our approach also discovers the trajectory itself.”
Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges (2021, background): “This can be done under the hypothesis that a same task can be tried out multiple times by the robot, by using iterative learning control [213] (ILC).”
Iterative Learning Control for Variable Pass Length Systems (2011, background): “If a low-pass filter is used instead, which is a common choice in many applications [1], then convergence cannot be guaranteed for all sequences of pass lengths {nj} and disturbances v.”
Experimentally verified generalized KYP Lemma based iterative learning control design (2016, methodology): “An overview of developments up to their dates of publication can be found in, e.g., 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.”
Robust trajectory optimization under frictional contact with iterative learning (2015, methodology): “In this work, we utilize ideas from previous work and use an iterative learning approach based on a binary search method and a robust trajectory optimization to learn the uncertainties from modeling and execution.”
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.”
A survey on iterative learning control for nonlinear systems (2011, methodology): “By using the lifting technique (Bristow et al. 2006) or supervector transformation (Ahn et al. 2007), discrete-time linear time-varying systems in a finite-time interval can be remodelled as an augmented matrix P between the input and output vectors ( u , y ), that is, y ¼ P u , where the matrix P…”
Actor-critic reinforcement learning for tracking control in robotics (2016, background): “Two widely-used learning control schemes are iterative learning control (ILC) [3] and repetitive control (RC) [4].”