Structured robust synthesis with parameter-dependent d-scales

Balint Patartics, Balint Vanek, Peter Seiler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Structured robust control synthesis has been actively researched in recent years. The advantage of any structured design technique is that it is capable of bringing advanced optimization-based methods to real-life applications. Drawing on a recently published paper, a novel algorithm is presented for structured control synthesis against mixed uncertainty. The method is iterative, alternating between collecting worst-case perturbation samples of the parametric uncertainty, and designing a controller for all the samples. During the synthesis procedure, the controller is designed for a set of plants with structured dynamic uncertainty employing a process similar to the classical D-K iteration. The key feature of this algorithm is that separate D-scales are assigned to the individual samples. Two numerical examples are provided, one specifically chosen to highlight the advantage of this approach.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1800-1805
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period7/10/197/12/19

Bibliographical note

Funding Information:
VI. ACKNOWLEDGMENTS The authors acknowledge useful discussions with R.S. da Silva de Aguiar, P. Apkarian, and D. Noll. P. Seiler acknowledges funding from the Hungarian Academy of Sciences Institute for Computer Science and Control (MTA-SZTAKI). The research leading to these results is part of the FLEXOP project. This project has received funding from the Horizon 2020 research and innovation programme of the European Union under grant agreement No 636307. The research was also supported by the ÚNKP-18-3 New National Excellence Pro-gram of the Ministry of Human Capacities. REFERENCES

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