A strong tracking extended Kalman observer for nonlinear discrete-time systems

Boutayeb, Mohamed; Aubry, Didier

doi:10.1109/9.780419

Cited by 227 publications

(124 citation statements)

References 16 publications

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“…Similar formulation has been used in (Boutayeb & Aubry, 1999) and (Xiong et al, 2006) to analyze the stability of the EKF and the UKF respectively. Using (17), the real error covariance matrix of the prediction error can be approximated by…”

Section: Stability Analysismentioning

confidence: 99%

Adaptive Robust Extended Kalman Filter

Xiong¹,

Zhang²,

Liu³

2009

Kalman Filter Recent Advances and Applications

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Section: Stability Analysismentioning

confidence: 99%

Adaptive Robust Extended Kalman Filter

Xiong¹,

Zhang²,

Liu³

2009

Kalman Filter Recent Advances and Applications

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“…With the same reasoning used in [24], we determine domains in which (17) is satisfactory. Under the following assumption:…”

Section: Convergence Analysismentioning

confidence: 99%

“…Consequently, in the same reasoning of [24] and [26], and so that the EKE ensures local asymptotic convergence, we must verify the following conditions:…”

Section: {} Kkmentioning

confidence: 99%

See 1 more Smart Citation

Real Time Dynamic State Estimation for Power System

Thabet¹,

Boutayeb²,

Abdelkrim³

2012

IJCA

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This paper investigates a method for the state estimation of nonlinear systems described by a class of differentialalgebraic equation (DAE) models using the extended Kalman filter. The method involves the use of a transformation from a DAE to ordinary differential equation (ODE). A relevant dynamic power systems model using decoupled techniques will be proposed. The estimation technique consists of a state estimator based on the EKF technique as well as local stability analysis. High performances are illustrated through a real time application on 5 buses test system with DSP device (Dspace DS1104).

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“…State observers using Extended Kalman Filters (EKFs) provide another method of transforming nonlinear systems (Boutayeb and Aubry, 1999;Khémiri et al, 2011). In particular, Besançon et al (2010) exploit an adaptive observer based on Kalman filters for physical systems to which Liénard transformations can be applied.…”

Section: Introductionmentioning

confidence: 99%

State estimation for a class of nonlinear systems

Schwaller¹,

Ensminger²,

Dresp-Langley³

et al. 2013

International Journal of Applied Mathematics and Computer Science

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We propose a new type of Proportional Integral (PI) state observer for a class of nonlinear systems in continuous time which ensures an asymptotic stable convergence of the state estimates. Approximations of nonlinearity are not necessary to obtain such results, but the functions must be, at least locally, of the Lipschitz type. The obtained state variables are exact and robust against noise. Naslin's damping criterion permits synthesizing gains in an algebraically simple and efficient way. Both the speed and damping of the observer response are controlled in this way. Model simulations based on a Sprott strange attractor are discussed as an example.

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A strong tracking extended Kalman observer for nonlinear discrete-time systems

Cited by 227 publications

References 16 publications

Adaptive Robust Extended Kalman Filter

Adaptive Robust Extended Kalman Filter

Real Time Dynamic State Estimation for Power System

State estimation for a class of nonlinear systems

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