On Data-driven Attack-resilient Gaussian Process Regression for Dynamic Systems

Hunmin Kim, Pinyao Guo, Minghui Zhu, Peng Liu

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

Abstract

paper studies attack-resilient Gaussian process regression of partially unknown nonlinear dynamic systems subject to sensor attacks and actuator attacks. The problem is formulated as the joint estimation of states, attack vectors, and system functions of partially unknown systems. We propose a new learning algorithm by incorporating our recently developed unknown input and state estimation technique into the Gaussian process regression algorithm. Stability of the proposed algorithm is formally studied. We also show that average case learning errors of system function approximation are diminishing if the number of state estimates whose estimation errors are non-zero is bounded by a constant. We demonstrate the performance of the proposed algorithm by numerical simulations on the IEEE 68-bus test system.

Original languageEnglish (US)
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2981-2986
Number of pages6
ISBN (Electronic)9781538682661
DOIs
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: Jul 1 2020Jul 3 2020

Publication series

NameProceedings of the American Control Conference
Volume2020-July
ISSN (Print)0743-1619

Conference

Conference2020 American Control Conference, ACC 2020
CountryUnited States
CityDenver
Period7/1/207/3/20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Kim, H., Guo, P., Zhu, M., & Liu, P. (2020). On Data-driven Attack-resilient Gaussian Process Regression for Dynamic Systems. In 2020 American Control Conference, ACC 2020 (pp. 2981-2986). [9147328] (Proceedings of the American Control Conference; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC45564.2020.9147328