Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

Leontine Aarnoudse; Peter den Toom; Tom Oomen

doi:10.1016/j.conengprac.2024.106152

Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

Leontine Aarnoudse^*, Peter den Toom, Tom Oomen

^*Corresponding author for this work

Team Jan-Willem van Wingerden

Research output: Contribution to journal › Article › Scientific › peer-review

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Abstract

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

Original language	English
Article number	106152
Number of pages	10
Journal	Control Engineering Practice
Volume	154
DOIs	https://doi.org/10.1016/j.conengprac.2024.106152
Publication status	Published - 2025

Keywords

Data-driven control
Gradient estimation
Iterative feedback tuning
MIMO systems

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10.1016/j.conengprac.2024.106152

1-s2.0-S0967066124003113-mainFinal published version, 1.31 MBLicence: CC BY-NC-ND

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abstract = "Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.",

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AB - Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

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Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

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Keywords

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