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

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

Journal Article (2025)

Author(s)

Leontine Aarnoudse (Eindhoven University of Technology)

Peter den Toom (Eindhoven University of Technology)

Tom Oomen (Eindhoven University of Technology, TU Delft - Mechanical Engineering)

Research Group

Team Jan-Willem van Wingerden

MIMO systems Data-driven control Gradient estimation Iterative feedback tuning

DOI related publication

https://doi.org/10.1016/j.conengprac.2024.106152 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:777ba863-a12a-4fe8-b542-050ebc0e7c47

More Info

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Publication Year

2025

Language

English

Research Group

Team Jan-Willem van Wingerden

Volume number

154

Article number

106152

Downloads counter

252

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.