We introduce an approach to efficiently tune LQR controllers for linear time-invariant systems to match a prescribed closed-loop behavior, such as the one given by a reference model. The proposed approach is able to efficiently tune the LQR controller, even for high dimensional systems and is superior in terms of achieved tracking performance and other criteria with respect to global optimization methods commonly used for black-box, simulation-based, automated tuning.

Tuning LQR Controllers: A Sensitivity-Based Approach

Masti D.;Zanon M.;Bemporad A.
2021

Abstract

We introduce an approach to efficiently tune LQR controllers for linear time-invariant systems to match a prescribed closed-loop behavior, such as the one given by a reference model. The proposed approach is able to efficiently tune the LQR controller, even for high dimensional systems and is superior in terms of achieved tracking performance and other criteria with respect to global optimization methods commonly used for black-box, simulation-based, automated tuning.
Identification for control
machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/19431
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