Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy

L.I.M. Aarnoudse; A. Pavlov; Tom A.E. Oomen

doi:10.1016/j.ifacol.2023.10.1907

Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy

L.I.M. Aarnoudse (Corresponding author)
, A. Pavlov
, Tom A.E. Oomen

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

360 Downloads (Pure)

Abstract

Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.

Original language	English
Pages (from-to)	1889-1894
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1907
Publication status	Published - 1 Jul 2023
Event	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/

Keywords

Iterative learning control
variable-gain control
nonlinear control
feedforward control
mechatronic systems

Access to Document

10.1016/j.ifacol.2023.10.1907

Nonlinear_ILC_IFAC2023Final author version , 788 KBLicence: CC BY-NC-ND
pdfFinal published version, 725 KBLicence: CC BY-NC-ND

Cite this

@article{34cc19651cd64673a364fe35776fb0c4,

title = "Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy",

abstract = "Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.",

keywords = "Iterative learning control, variable-gain control, nonlinear control, feedforward control, mechatronic systems",

author = "L.I.M. Aarnoudse and A. Pavlov and Oomen, \{Tom A.E.\}",

year = "2023",

month = jul,

day = "1",

doi = "10.1016/j.ifacol.2023.10.1907",

language = "English",

volume = "56",

pages = "1889--1894",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier",

number = "2",

note = "22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) ; Conference date: 09-07-2023 Through 14-07-2023",

url = "https://www.ifac2023.org/",

}

TY - JOUR

T1 - Nonlinear Iterative Learning Control

T2 - 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress)

AU - Aarnoudse, L.I.M.

AU - Pavlov, A.

AU - Oomen, Tom A.E.

N1 - Conference code: 22

PY - 2023/7/1

Y1 - 2023/7/1

N2 - Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.

AB - Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.

KW - Iterative learning control

KW - variable-gain control

KW - nonlinear control

KW - feedforward control

KW - mechatronic systems

UR - https://www.scopus.com/pages/publications/85184487056

U2 - 10.1016/j.ifacol.2023.10.1907

DO - 10.1016/j.ifacol.2023.10.1907

M3 - Conference article

SN - 2405-8963

VL - 56

SP - 1889

EP - 1894

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

Y2 - 9 July 2023 through 14 July 2023

ER -

Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this