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.
Auto-tuning of reference models in direct data-driven control / Masti, D.; Breschi, V.; Formentin, S.; Bemporad, A.. - In: AUTOMATICA. - ISSN 0005-1098. - 155:(2023), p. 111110. [10.1016/j.automatica.2023.111110]
Auto-tuning of reference models in direct data-driven control
Masti D.;Breschi V.;Bemporad A.
2023
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
