Optimal input design for direct data-driven tuning of model-reference controllers

Authors:
Simone Formentin;Alireza Karimi;Sergio M. Savaresi
Affiliations:
-;-;-
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 4
Cited 0

Optimal experiment designs with respect to the intended model application

Automatica (Journal of IFAC)
Approximate feedback linearization of discrete-time non-linear systems using virtual input direct design

Systems & Control Letters
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers

Automatica (Journal of IFAC)
From experiment design to closed-loop control

Automatica (Journal of IFAC)

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Abstract

In recent years, direct data-driven controller tuning methods have been proposed as an alternative to the standard model-based approach for model-reference control design. In this work, the problem of input design for noniterative direct data-driven techniques, namely Virtual Reference Feedback Tuning (VRFT) and noniterative Correlation-based Tuning (CbT), is investigated. For bounded input energy, the excitation signal is designed such that the expected value of the considered control cost is reduced. The above strategy is numerically tested on a benchmark example.