Extraction of informative subsets from routine operating data for use in data-driven control

Garcia, Cristiane Silva; Bazanella, Alexandre Sanfelice

doi:10.1109/cdc40024.2019.9029265

Cited by 4 publications

(1 citation statement)

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“…In our previous works, we have adapted two data selection criteria, originally developed for system identification, to be used with the Virtual Reference Feedback Tuning (VRFT) method Bazanella, 2019, 2020). Garcia and Bazanella (2019) approached the case of full-order controllers, that is, the ideal controller may be constructed using the available controller structure. The reduced-order controllers case was considered in Garcia and Bazanella (2020), where we have shown that only one of the criteria could be applied to this specific case.…”

Section: Introductionmentioning

confidence: 99%

Virtual Reference Feedback Tuning With Data Selection Criteria Applied to a Thermal Process

Garcia

Bazanella

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Adjusting the controller's parameters using a data-driven (DD) methodology usually requires data gathered from a specific experiment performed in the process, which may be a time consuming task for the designer. To avoid this task, routine operating data could be used instead. However, combining those raw data along with a DD method almost certainly results in inappropriate tuning. Therefore, it is advisable to pre-select the useful information before estimating the controller's parameters. The present work is an extension of our previous works, where two data selection criteria were applied to the Virtual Reference Feedback Tuning (VRFT) method. In the present work, we have combined the application of those criteria to select the relevant data subsets. Moreover, the controller's parameters are estimated using not only VRFT's original solution, known as Ordinary Least Squares (OLS), but also the Data Least Squares (DLS) solution. The feasibility of the proposed solution is evaluated through experiments carried out in a thermal process.

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Section: Introductionmentioning

confidence: 99%