State estimation and unknown input reconstruction via both reduced-order and high-order sliding mode observers

Zhu, Fanglai

doi:10.1016/j.jprocont.2011.07.007

Cited by 100 publications

(61 citation statements)

References 20 publications

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“…More recently, Floquet and Zhu have presented methods for systems with a higher relative degree [42,43]. The approaches of both authors rely on the use of high order sliding mode observers to estimate output derivatives.…”

Section: Review Of Unknown Input Estimation Approachesmentioning

confidence: 99%

Observers with Dual Spatially Separated Sensors for Enhanced Estimation: Industrial, Automotive, and Biomedical Applications

Rajamani

Wang

Nelson³

et al. 2017

IEEE Control Syst.

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Section: Review Of Unknown Input Estimation Approachesmentioning

confidence: 99%

Observers with Dual Spatially Separated Sensors for Enhanced Estimation: Industrial, Automotive, and Biomedical Applications

Rajamani

Wang

Nelson³

et al. 2017

IEEE Control Syst.

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“…These two methods are both have limitations, and hard to meet requirements. Researches on the case where the number of the independent faults less than that of the independent measurements seem to be less [11].…”

Section: Introductionmentioning

confidence: 99%

A Fault Estimation Method Based on UIO Robust Residual Generators

Liu

Feng

Zhang

et al. 2016

MATEC Web of Conferences

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Abstract. This paper proposed a fault estimation method based on robust residual generators for a linear system. A system with additive actuator or component faults was considered in the case where the number of the independent faults was larger than that of the independent measurements. This method was achieved based on UIO robust residual generators and there was no need to design extra fault estimators. In this method, fault estimation was achieved via three steps. First, codingsets, which describe the sensitivity relationship between faults and generators, are designed. Second, a bank of robust residual generators are designed according to the coding sets. Finally, fault estimation is achieved by using the result of fault isolation and the output of robust residual generators. A sufficient condition on the application of the method was given and the asymptotic convergence property of the estimation error by using the method was proved. Simulation results demonstrate the effectiveness of the method.

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“…Owing to the increasing demands of reliability, safety for the modern complex systems, observer design for system with unknown inputs, which is known as unknown input observer (UIO), has received much more attention in the past decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, we find that in the field of UIO, much more attention is put on linear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…However, we find that in the field of UIO, much more attention is put on linear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Only a few results can be found for some specific nonlinear systems, such as for Lipschitz or one-sided Lipschitz nonlinear systems [15,16].…”

Section: Introductionmentioning

confidence: 99%

Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

Guo

Zhu

2015

Int. J. Control Autom. Syst.

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This paper deals with the unknown input observer design for Takagi-Sugeno (T-S) fuzzy models subjected to measurement noise and stochastic noise. The method applies the singular system theory by considering the measurement noise as an augmented system state, and then an unknown input observer based on the techniques of singular systems is developed to estimate both the system states and measurement noise simultaneously. Under a necessary assumption, the error dynamic system of the observer is free from the unknown inputs. And the observer gain matrix is determined by means of minimum covariance matrix of state residual. Two simulation examples are given to illustrate the correctness and effectiveness of the proposed method.

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State estimation and unknown input reconstruction via both reduced-order and high-order sliding mode observers

Cited by 100 publications

References 20 publications

Observers with Dual Spatially Separated Sensors for Enhanced Estimation: Industrial, Automotive, and Biomedical Applications

Observers with Dual Spatially Separated Sensors for Enhanced Estimation: Industrial, Automotive, and Biomedical Applications

A Fault Estimation Method Based on UIO Robust Residual Generators

Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

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