Recursive state estimation for discrete-time nonlinear systems with event-triggered data transmission, norm-bounded uncertainties and multiple missing measurements

Summary In this paper, the event‐triggered nonlinear filtering problem is investigated for nonlinear dynamic systems over a wireless sensor network with packet dropout. Measurements are transmitted to a remote estimator only when a specific event happens for a reduction of communication cost. An event‐triggered unscented Kalman filter related to trigger threshold is derived. It is shown that the prediction error covariance of the proposed filter is bounded and converges to a steady value if the threshold and packet dropout rate are small enough. Sufficient conditions are obtained to ensure stochastic stability of the filter, where a critical value of the threshold exists. Two examples are given to illustrate the effectiveness of the proposed filter. Copyright © 2017 John Wiley & Sons, Ltd.

“…Note that the received signal y k is influenced by the designed event‐triggered condition, and the system dynamics, which results in that the terms containing

{\overset{y}{true}}_{k} - y_{k + 1}

in add extra difficulty for designing the nonlinear filter. Motivated by and , detailed derivations are given to obtain an upper bound of

{\overset{normal Σ}{true}}_{k + 1 | k + 1}

.…”

Section: Design Of Event‐triggered Unscented Kalman Filter With Packementioning

confidence: 99%

“…Example In this example, a third‐order nonlinear system is used to illustrate the effectiveness of our results . The nonlinear system is represented as follows:

\{\begin{matrix} x_{k + 1} = ([], \begin{matrix} array 0.05 x_{2, k} + \sin (x_{3, k}) \\ array x_{1, k} + e^{- 0.05 x_{3, k}} + 10 \\ array 0.2 x_{1, k} (x_{2, k} + x_{3, k}) \end{matrix}) + D_{k} ω_{k} \\ y_{k} = \cos (x_{1, k}) + x_{2, k} x_{3, k} + ν_{k} \end{matrix}

where x k =[ x 1, k x 2, k x 3, k ] T , and D k =[111] T .…”

Section: Numerical Simulationmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event‐triggered UKF for nonlinear dynamic systems with packet dropout

Xia

et al. 2017

“…Networked control systems (NCSs) have received much attention in recent years because of their easy installation, low maintenance costs, and flexible control structures. However, there are some troubles induced by networks inevitably, such as random delays, packet dropouts, and quantization errors .…”

Section: Introductionmentioning

confidence: 99%

Modified particle filter and Gaussian filter with packet dropouts

Yang

Fang

2018

Self Cite

This technical note is concerned with the nonlinear filtering for networked control systems. First, the modified particle filter algorithm with intermittent observations is proposed and the conditional Cramér-Rao lower (CRL) bound with packet dropouts for nonlinear non-Gaussian system is derived. Second, an upper bound for the CRL bound of the Gaussian filter with packet losses is obtained by constructing a linear Gaussian-Markovian networked system because of the complexity in direct analysis and computation. Third, a sufficient condition is given for the bounded expectation of the CRL bound, which is the necessary condition for bounded mean-square error covariance. Finally, an example illustrates the effectiveness of the proposed filter. KEYWORDSCramér-Rao lower bound, networked control systems, packet dropout, particle filter Int J Robust Nonlinear Control. 2018;28:2961-2975.wileyonlinelibrary.com/journal/rnc

Robust state estimation for wireless sensor networks with data‐driven communication

Liu

Wang

2017

Summary Robust state estimation problem for wireless sensor networks composed of multiple remote sensor nodes and a fusion node is investigated subject to a limitation on the communication rate. An analytical robust fusion estimator based on a data‐driven transmission strategy is derived to save the sensor energy consumption and reduce the network traffic congestion. The conditions guaranteeing the uniform boundedness of estimation errors of the robust fusion estimator are investigated. Numerical simulations are provided to show the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.