A practitioner's guide to noise handling strategies in data-driven predictive control

Andrea Sassella; Valentina Breschi; Simone Formentin

doi:10.1016/j.ifacol.2023.10.1795

A practitioner's guide to noise handling strategies in data-driven predictive control

Andrea Sassella, Valentina Breschi, Simone Formentin

Control Systems

Research output: Contribution to journal › Conference article › peer-review

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Abstract

Today's increasing availability of data is having a remarkable impact on control design. However, for data-driven control approaches to become widespread in practical applications, it is necessary to devise strategies that can effectively handle the presence of noise in the data used to design the controller. In this work, we analyse the existing approaches to deal with noisy measurements in data-driven predictive control (DDPC) and we highlight the advantages and downsides of each technique from a practitioner's perspective. Our qualitative conclusions are supported by the results obtained from two benchmark examples.

Original language	English
Pages (from-to)	1382-1387
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1795
Publication status	Published - Jul 2023
Event	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/

Keywords

Data-driven control
Noise handling
Predictive Control

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10.1016/j.ifacol.2023.10.1795

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AU - Breschi, Valentina

AU - Formentin, Simone

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PY - 2023/7

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AB - Today's increasing availability of data is having a remarkable impact on control design. However, for data-driven control approaches to become widespread in practical applications, it is necessary to devise strategies that can effectively handle the presence of noise in the data used to design the controller. In this work, we analyse the existing approaches to deal with noisy measurements in data-driven predictive control (DDPC) and we highlight the advantages and downsides of each technique from a practitioner's perspective. Our qualitative conclusions are supported by the results obtained from two benchmark examples.

KW - Data-driven control

KW - Noise handling

KW - Predictive Control

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DO - 10.1016/j.ifacol.2023.10.1795

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A practitioner's guide to noise handling strategies in data-driven predictive control

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