Resilient State Recovery Using Prior Measurement Support Information

Zheng, Yu; Anubi, Olugbenga Moses; Dixon, Warren E.

doi:10.1137/23M1611671

Resilient State Recovery Using Prior Measurement Support Information

Journal Article · Tue Oct 14 00:00:00 EDT 2025 · SIAM Journal on Control and Optimization

DOI:https://doi.org/10.1137/23M1611671· OSTI ID:3005772

^[1]; ^[1]; Dixon, Warren E. ^[2]

Florida State University, Tallahassee, FL (United States)
University of Florida, Gainesville, FL (United States)

Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation, and the underlying physics of such systems. By modeling attacks as additive adversary signals to a sparse subset of measurements, this resilient recovery problem can be formulated as an error correction problem. To achieve exact state recovery, most existing results require less than 50% of the measurement nodes to be compromised, which limits the resiliency of the estimators. In this paper, we show that observer resiliency can be further improved by incorporating data-driven prior information. Here, we provide an analytical bridge between the precision of prior information and the resiliency of the estimator. By quantifying the relationship between the estimation error of the weighted ℓ₁ observer and the precision of the support prior, this quantified relationship provides guidance for the estimator’s weight design to achieve optimal resiliency. Several numerical simulations and an application case study are presented to validate the theoretical claims.

View Accepted Manuscript (DOE)

Research Organization:: Florida State University, Tallahassee, FL (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: Florida State University (FSU) Council on Research and Creativity (CRC); USDOE

Grant/Contract Number:: CR0000028

OSTI ID:: 3005772

Journal Information:: SIAM Journal on Control and Optimization, Journal Name: SIAM Journal on Control and Optimization Journal Issue: 5 Vol. 63; ISSN 0363-0129; ISSN 1095-7138

Publisher:: Society for Industrial and Applied Mathematics (SIAM)Copyright Statement

Country of Publication:: United States

Language:: English

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