Efficient MIMO Iterative Feedback Tuning via Randomization

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

doi:10.1109/CDC49753.2023.10383883

Efficient MIMO Iterative Feedback Tuning via Randomization

L.I.M. Aarnoudse, Tom A.E. Oomen

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

3 Citations (Scopus)

195 Downloads (Pure)

Abstract

Iterative feedback tuning (IFT) enables the tuning of feedback controllers based on measured data without the need for a parametric model. The aim of this paper is to develop an efficient method for MIMO IFT that reduces the required number of experiments. Using a randomization technique, an unbiased gradient estimate is obtained from a single dedicated experiment, regardless of the size of the MIMO system. This gradient estimate is employed in a stochastic gradient descent algorithm. Simulation examples illustrate that the approach reduces the number of experiments required to converge.

Original language	English
Title of host publication	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Publisher	Institute of Electrical and Electronics Engineers
Pages	4512-4517
Number of pages	6
ISBN (Electronic)	979-8-3503-0124-3
DOIs	https://doi.org/10.1109/CDC49753.2023.10383883
Publication status	Published - 19 Jan 2024
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 Conference number: 62

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Abbreviated title	CDC 2023
Country/Territory	Singapore
City	Singapore
Period	13/12/23 → 15/12/23

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10.1109/CDC49753.2023.10383883

2023_SAIFT_preprintFinal author version , 350 KBLicence: CC BY-NC-ND
pdfFinal published version, 398 KBLicence: TAVERNE

Cite this

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AB - Iterative feedback tuning (IFT) enables the tuning of feedback controllers based on measured data without the need for a parametric model. The aim of this paper is to develop an efficient method for MIMO IFT that reduces the required number of experiments. Using a randomization technique, an unbiased gradient estimate is obtained from a single dedicated experiment, regardless of the size of the MIMO system. This gradient estimate is employed in a stochastic gradient descent algorithm. Simulation examples illustrate that the approach reduces the number of experiments required to converge.

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T2 - 62nd IEEE Conference on Decision and Control, CDC 2023

Y2 - 13 December 2023 through 15 December 2023

ER -

Efficient MIMO Iterative Feedback Tuning via Randomization

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