In this work we propose a kernel-based robust nonparametric approach to direct data-driven control of linear systems, in the presence of bounded noise affecting the measurements data. First we formulate the problem of designing a controller in order to match the behavior of a given reference model. Then, we design the controller by applying previous results by some of the authors in the field of kernel-based nonparametric error-in-variables identification. Finally, we show the effectiveness of the presented technique by means of two simulation examples.

العنوان

18th IFAC Symposium on System Identification SYSID 2018

الناشر

sciencedirect

بلد الناشر

الولايات المتحدة الأمريكية

Indexing

Scopus

معامل التأثير

None

نوع المنشور

Both (Printed and Online)

المجلد

51

السنة

2018

الصفحات

1026-103