A secure state estimation algorithm for nonlinear systems under sensor attacks
2020 (English)In: Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 5743-5748Conference paper, Published paper (Refereed)
Abstract [en]
The state estimation of continuous-time nonlinear systems in which a subset of sensor outputs can be maliciously controlled through injecting a potentially unbounded additive signal is considered in this paper. Analogous to our earlier work for continuous-time linear systems in [1], we term the convergence of the estimates to the true states in the presence of sensor attacks as 'observability under M attacks', where M refers to the number of sensors which the attacker has access to. Unlike the linear case, we only provide a sufficient condition such that a nonlinear system is observable under M attacks. The condition requires the existence of asymptotic observers which are robust with respect to the attack signals in an input-to-state stable sense. We show that an algorithm to choose a compatible state estimate from the state estimates generated by a bank of observers achieves asymptotic state reconstruction. We also provide a constructive method for a class of nonlinear systems to design state observers which have the desirable robustness property. The relevance of this study is illustrated on monitoring the safe operation of a power distribution network.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020. p. 5743-5748
Keywords [en]
Linear systems, Nonlinear control systems, Observability, State estimation, Asymptotic observer, Constructive methods, Continuous time nonlinear systems, Continuous-time linear systems, Input to state stable, Power distribution network, Robustness properties, State estimation algorithms, Continuous time systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-301200DOI: 10.1109/CDC42340.2020.9304318ISI: 000717663404096Scopus ID: 2-s2.0-85099876293OAI: oai:DiVA.org:kth-301200DiVA, id: diva2:1591761
Conference
59th IEEE Conference on Decision and Control, CDC 2020, 14 December 2020 through 18 December 2020
Note
QC 20210907
2021-09-072021-09-072023-04-05Bibliographically approved