The paper deals with the problem of designing controllers from experimental data. We propose a non-iterative direct approach in which the parameters of a controller of a prescribed order and structure are optimized with respect to a relevant performance criterion. The proposed approach builds upon the so-called unfalsified control theory. This is the key point which makes it possible to derive simple and intuitive relations between the choice of the performance criterion to optimize and closed-loop stability conditions, thus making it possible to derive a data-driven controller tuning procedure incorporating simple stability tests. An example is presented to substantiate the analysis.

Unfalsified Approach to Data-Driven Control Design

D. Selvi;
2014-01-01

Abstract

The paper deals with the problem of designing controllers from experimental data. We propose a non-iterative direct approach in which the parameters of a controller of a prescribed order and structure are optimized with respect to a relevant performance criterion. The proposed approach builds upon the so-called unfalsified control theory. This is the key point which makes it possible to derive simple and intuitive relations between the choice of the performance criterion to optimize and closed-loop stability conditions, thus making it possible to derive a data-driven controller tuning procedure incorporating simple stability tests. An example is presented to substantiate the analysis.
2014
978-1-4673-6090-6
control design; data-driven optimization; unfalsified control
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/7184
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