Receding horizon state estimation for LPV systems in the presence of non-uniform measurements

Isaza, Jhon A.; Martínez, John J.; Botero, Héctor

doi:10.1109/ccac.2017.8276424

Cited by 1 publication

(1 citation statement)

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“…Ref. [ 26 ] presents a method for nonlinear state estimation of biomass in a batch bioprocess, which employs the EKF with a sample-state augmentation method to incorporate delayed measurements. A dynamic state estimation algorithm is proposed in [ 27 ], which employs Holt’s two-parameter exponential smoothing and extended Kalman filtering techniques.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks

Wang

Liu

2023

Sensors

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In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.

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Section: Introductionmentioning

confidence: 99%