Estimation for Nonlinear Dynamical Systems over Packet-Dropping Networks

Zhou, Jin; Ko, Chih-Kai; Murray, Richard M.

doi:10.1109/acc.2007.4283063

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…Additionally, there is a difference in covariance matrix calculation. FASE makes use of the error covariance matrix Σ k+1 , which changes Equation (25) to:…”

Section: Residual Analysismentioning

confidence: 99%

Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

2019

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This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

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“…Additionally, there is a difference in covariance matrix calculation. FASE makes use of the error covariance matrix Σ k+1 , which changes Equation (25) to:…”

Section: Residual Analysismentioning

confidence: 99%

Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

2019

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“…Simulated Models Two nonlinear models are used to analyze the proposed results. The models used are described below: First model: The first numerical example considered in this section is a first-order nonlinear discrete model [17] given by:…”

Section: A Ukf With Intermittent Observationmentioning

confidence: 99%

On the performance of unscented Kalman filter with intermittent observations

Foletto

Moreno

2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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The insertion of the communication network in feedback control loop makes the analysis and design of wireless networked control systems (WNCS) more complex, induces challenges in the form of: delays and packet losses, that degrade the control system's performance and even cause instability. From the viewpoint of application, the main challenge found in wireless networked control system (WNCS) is the packet loss, thus the use of observers as estimators states has been presented as an alternative solution to treat intermittent measurements. The purpose of this paper is study the statistical convergence properties of the estimation error covariance of unscented Kalman filter with intermittent observation (UKFI), showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs.

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“…However, state estimation for nonlinear NCS has not been widely investigated yet. In Jin et al (2007), two approaches were discussed in the presence of a packetdropping network, namely the extended Kalman filter (EKF) and the moving horizon estimation (MHE). A moving horizon observer has been designed in Philipp and Lohmann (2009), capable of dealing with variable time delays and packet drops.…”

Section: Introductionmentioning

confidence: 99%