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Asymptotic Stabilization over Encrypted Data with Limited Controller Capacity and Time-varying Quantizer
Seoul Natl Univ Sci & Technol, Dept Elect & Informat Engn, Seoul, South Korea..
TU Dortmund, Dept Mech Engn, Dortmund, Germany..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1835-2963
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-9940-5929
2022 (English)In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 7762-7767Conference paper, Published paper (Refereed)
Abstract [en]

We consider a problem of implementing dynamic controllers over encrypted data for asymptotic stabilization of closed-loop systems. Though a time-varying quantizer is used and it can be infinitesimally fine with time, a major issue is that the underlying space for encrypted messages is unavoidably finite and the controller receives a limited amount of quantized data. To resolve this issue, the proposed method takes advantage of the state matrix consisting of integers, which enables the controller to generate only lower bits of the same output without computing the upper bits. Whenever a portion of the upper bits of output has converged, the computation scope can be moved further lower, receiving only lower bits of the measurement. The quantization is scheduled and the size of the message space is predetermined from the convergence rate, so that the feedback input is restored from the outcome of the lower bits, no matter how fine quantization is performed in the end. As a consequence, asymptotic stabilization can be achieved by encrypted operation, despite the limited controller capacity.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 7762-7767
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-326442DOI: 10.1109/CDC51059.2022.9993215ISI: 000948128106072Scopus ID: 2-s2.0-85146979169OAI: oai:DiVA.org:kth-326442DiVA, id: diva2:1754210
Conference
IEEE 61st Conference on Decision and Control (CDC), DEC 06-09, 2022, Cancun, MEXICO
Note

QC 20230503

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2023-05-03Bibliographically approved

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Sandberg, HenrikJohansson, Karl H.

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