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.;Bemporad A.
2023-01-01

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.
2023
Autotuning
Data-based control
Direct data-driven control
Model-reference control
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/25099
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