A Gaussian Process approach to multiple internal models in repetitive control

Noud Mooren; Gert Witvoet; Tom Oomen

doi:10.1109/AMC51637.2022.9729281

A Gaussian Process approach to multiple internal models in repetitive control

Noud Mooren, Gert Witvoet, Tom Oomen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Disturbances that come from multiple originating domains, e.g., time, position, or commutation-angle domain, are often encountered in practice due to the increasing complexity of mechatronic systems. The aim of this paper is to present a generalized approach that enables asymptotic rejection of multi-dimensional disturbances which are periodic in the different originating domains, e.g., if speed changes, then spatially-periodic disturbances manifest themselves differently in the time domain. A multi-dimensional Gaussian process (GP) based internal model is employed in conjunction with a traditional repetitive control (RC) setting using non-equidistant observations, allowing to learn a multidimensional buffer for RC. A case study with a spatio-temporal disturbance confirms the benefit of this method.

Original language	English
Title of host publication	2022 IEEE 17th International Conference on Advanced Motion Control (AMC)
Publisher	Institute of Electrical and Electronics Engineers
Pages	274-279
Number of pages	6
ISBN (Electronic)	978-1-7281-7711-3
DOIs	https://doi.org/10.1109/AMC51637.2022.9729281
Publication status	Published - 11 Mar 2022
Event	17th IEEE International Conference on Advanced Motion Control, AMC 2022 - Padova, Italy Duration: 18 Feb 2022 → 20 Feb 2022 Conference number: 17 http://static.gest.unipd.it/AMC2022/

Conference

Conference	17th IEEE International Conference on Advanced Motion Control, AMC 2022
Abbreviated title	AMC 2022
Country/Territory	Italy
City	Padova
Period	18/02/22 → 20/02/22
Internet address	http://static.gest.unipd.it/AMC2022/

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10.1109/AMC51637.2022.9729281

MoorenWitOom2022bFinal author version , 1.51 MB

Cite this

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title = "A Gaussian Process approach to multiple internal models in repetitive control",

abstract = "Disturbances that come from multiple originating domains, e.g., time, position, or commutation-angle domain, are often encountered in practice due to the increasing complexity of mechatronic systems. The aim of this paper is to present a generalized approach that enables asymptotic rejection of multi-dimensional disturbances which are periodic in the different originating domains, e.g., if speed changes, then spatially-periodic disturbances manifest themselves differently in the time domain. A multi-dimensional Gaussian process (GP) based internal model is employed in conjunction with a traditional repetitive control (RC) setting using non-equidistant observations, allowing to learn a multidimensional buffer for RC. A case study with a spatio-temporal disturbance confirms the benefit of this method.",

author = "Noud Mooren and Gert Witvoet and Tom Oomen",

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doi = "10.1109/AMC51637.2022.9729281",

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publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "17th IEEE International Conference on Advanced Motion Control, AMC 2022, AMC 2022 ; Conference date: 18-02-2022 Through 20-02-2022",

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}

Mooren, N, Witvoet, G & Oomen, T 2022, A Gaussian Process approach to multiple internal models in repetitive control. in 2022 IEEE 17th International Conference on Advanced Motion Control (AMC)., 9729281, Institute of Electrical and Electronics Engineers, pp. 274-279, 17th IEEE International Conference on Advanced Motion Control, AMC 2022, Padova, Italy, 18/02/22. https://doi.org/10.1109/AMC51637.2022.9729281

A Gaussian Process approach to multiple internal models in repetitive control. / Mooren, Noud; Witvoet, Gert ; Oomen, Tom.
2022 IEEE 17th International Conference on Advanced Motion Control (AMC). Institute of Electrical and Electronics Engineers, 2022. p. 274-279 9729281.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - A Gaussian Process approach to multiple internal models in repetitive control

AU - Mooren, Noud

AU - Witvoet, Gert

AU - Oomen, Tom

N1 - Conference code: 17

PY - 2022/3/11

Y1 - 2022/3/11

N2 - Disturbances that come from multiple originating domains, e.g., time, position, or commutation-angle domain, are often encountered in practice due to the increasing complexity of mechatronic systems. The aim of this paper is to present a generalized approach that enables asymptotic rejection of multi-dimensional disturbances which are periodic in the different originating domains, e.g., if speed changes, then spatially-periodic disturbances manifest themselves differently in the time domain. A multi-dimensional Gaussian process (GP) based internal model is employed in conjunction with a traditional repetitive control (RC) setting using non-equidistant observations, allowing to learn a multidimensional buffer for RC. A case study with a spatio-temporal disturbance confirms the benefit of this method.

AB - Disturbances that come from multiple originating domains, e.g., time, position, or commutation-angle domain, are often encountered in practice due to the increasing complexity of mechatronic systems. The aim of this paper is to present a generalized approach that enables asymptotic rejection of multi-dimensional disturbances which are periodic in the different originating domains, e.g., if speed changes, then spatially-periodic disturbances manifest themselves differently in the time domain. A multi-dimensional Gaussian process (GP) based internal model is employed in conjunction with a traditional repetitive control (RC) setting using non-equidistant observations, allowing to learn a multidimensional buffer for RC. A case study with a spatio-temporal disturbance confirms the benefit of this method.

U2 - 10.1109/AMC51637.2022.9729281

DO - 10.1109/AMC51637.2022.9729281

M3 - Conference contribution

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BT - 2022 IEEE 17th International Conference on Advanced Motion Control (AMC)

PB - Institute of Electrical and Electronics Engineers

T2 - 17th IEEE International Conference on Advanced Motion Control, AMC 2022

Y2 - 18 February 2022 through 20 February 2022

ER -

A Gaussian Process approach to multiple internal models in repetitive control

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