Auto-tuning of reference models in direct data-driven control

Daniele Masti; Valentina Breschi; Simone Formentin; Alberto Bemporad

doi:10.1016/j.automatica.2023.111110

Auto-tuning of reference models in direct data-driven control

Daniele Masti
, Valentina Breschi (Corresponding author)
, Simone Formentin
, Alberto Bemporad

Control Systems

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

5 Citations (Scopus)

106 Downloads (Pure)

Abstract

Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints, and it is designed to handle non-minimum phase dynamics. The effectiveness of the proposed approach is shown through three numerical case studies.

Original language	English
Article number	111110
Number of pages	11
Journal	Automatica
Volume	155
DOIs	https://doi.org/10.1016/j.automatica.2023.111110
Publication status	Published - Sept 2023

Funding

This paper was partially supported by the Italian Ministry of University and Research under the PRIN’17 project “Data-driven learning of constrained control systems”, contract no. 2017J89ARP . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Juan I. Yuz under the direction of Editor Alessandro Chiuso.

Funders	Funder number
Ministero dell’Istruzione, dell’Università e della Ricerca	2017J89ARP

Keywords

Autotuning
Data-based control
Direct data-driven control
Model-reference control

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10.1016/j.automatica.2023.111110

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AU - Formentin, Simone

AU - Bemporad, Alberto

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AB - Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints, and it is designed to handle non-minimum phase dynamics. The effectiveness of the proposed approach is shown through three numerical case studies.

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KW - Data-based control

KW - Direct data-driven control

KW - Model-reference control

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JO - Automatica

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ER -

Auto-tuning of reference models in direct data-driven control

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