An Optimization Framework for Resilient Batch Estimation in Cyber-Physical Systems
成果类型:
Article
署名作者:
Kircher, Alexandre; Bako, Laurent; Blanco, Eric; Benallouch, Mohamed
署名单位:
Ecole Centrale de Lyon; Institut National des Sciences Appliquees de Lyon - INSA Lyon; Universite Claude Bernard Lyon 1; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS)
刊物名称:
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN:
0018-9286
DOI:
10.1109/TAC.2021.3121223
发表日期:
2022
页码:
5246-5261
关键词:
sensors
mathematical models
Noise measurement
State estimation
estimation error
resilience
observability
cyber-physical systems (CPS)
optimal estimation
resilient estimators
secure state estimation
摘要:
This article proposes a class of resilient state estimators for linear time-varying discrete-time systems. The dynamic equation of the system is assumed to be affected by a bounded process noise. As to the available measurements, they are potentially corrupted by a noise of both dense and impulsive natures. The latter, in addition to being arbitrary in its form, need not be strictly bounded. In this setting, we construct the estimator as the set-valued map, which associates with the measurements the minimizing set of some appropriate performance functions. We consider a family of such performance functions, each of which yielding a specific instance of the proposed general estimation framework. It is then shown that the proposed class of estimators enjoys the property of resilience, i.e., it induces an estimation error, which, under certain conditions, is independent of the extreme values of the (impulsive) measurement noise. Hence, the estimation error may be bounded, while the measurement noise is virtually unbounded. Moreover, we provide several error bounds (in different configurations), whose expressions depend explicitly on the degree of observability of the system being observed and on the considered performance function. Finally, a few simulation results are provided to illustrate the resilience property.
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