Failure monitoring in a high performance hydrostatic actuation system using the extended Kalman filter

Chinniah, Yuvin; Burton, Richard; Habibi, Saeid

doi:10.1016/j.mechatronics.2006.04.004

Cited by 46 publications

(25 citation statements)

References 13 publications

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“…Although there have been considerable research on HST systems (Manring and Luecke, 1998;Kugi et al, 2000;Chinniah et al, 2006;Dasgupta et al, 2006) and robust diagnosis of continuous dynamical systems, to the knowledge of the authors there is no reported work on the model-based quantitative robust diagnosis of hydrostatic systems with uncertain system parameters. A similar type of system was modelled in Dasgupta et al, 2006, in which dynamic behaviour was studied through simulation and then it was experimentally validated.…”

Section: Fig 1 Standard Interconnection or Internal Feedback Loop Modelmentioning

confidence: 99%

Single fault isolation of a Hydrostatic Transmission System: Simulation and Experimentation

Samanta¹,

Ghoshal²

2013

JMSIC

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In this article, different methodologies in the area of system identification, parametric fault isolation, robustness in fault diagnosis have been surveyed and applied to a pedagogical HydroStatic Transmission (HST) system. A Bond Graph (BG) model for the system has been developed and then nonlinear regression analysis was done on the state equations, obtained from that model, to estimate the unknown parameters as a part of system identification. The test bench was designed to introduce faults in different components for the purpose of experimental validation of Fault Detection and Isolation (FDI). The model based single fault isolation was done by using structural analysis of some constraint relations, called Analytical Redundancy Relations (ARR), the numerical evaluation of which are called residuals. The robustness in fault isolation was addressed through Linear Fractional Transformation (LFT) approach to make residuals insensitive to uncertainty of system parameters, but sensitive to fault.

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Section: Fig 1 Standard Interconnection or Internal Feedback Loop Modelmentioning

confidence: 99%

Single fault isolation of a Hydrostatic Transmission System: Simulation and Experimentation

Samanta¹,

Ghoshal²

2013

JMSIC

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“…The sequential method also allows for diagnosis of changes in the values of parameters that are believed to be constant but that may undergo sudden or gradual shifts. This has been investigated for fault detection in hydraulic systems by Chinniah et al (2006) and in railway vehicle suspension by Li et al (2007). In a model of terrestrial carbon, if an area of land is cleared there could be a sudden shift in model parameters to describe the new condition of the land.…”

Section: M Trudinger Et Almentioning

confidence: 99%

Using the Kalman filter for parameter estimation in biogeochemical models

et al. 2008

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SUMMARYWe investigate application of nonlinear variants of the Kalman filter (KF) to sequential parameter estimation in biogeochemical models, with particular focus on two components of the statistical model: Q, the covariance of the stochastic forcing which we use to represent model error, and R, the observation error covariance matrix. We explored sensitivity of parameter estimates from the extended and ensemble Kalman filters (EKF and EnKF) to the choice of Q, R, initial parameters and ensemble size using pseudo-data from a simple yet highly nonlinear test model with many characteristics similar to real terrestrial biogeochemistry models. We found for our application that the use of inflated observation uncertainties led to the best and most stable parameter estimates. Although this reduced the rate of convergence to a solution, it also reduced the sensitivity of the solution to model error or ensemble size in the EnKF. Neither the use of model error for the parameters nor inflation of the state error covariance was particularly successful.

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“…However, these control schemes have the chattering problem due to the variable structure control scheme. Chinniah et al used a robust extended Kalman filter, which can estimate the viscous friction and effective bulk modulus, to detect faults in EHA systems (7) . Kaddissi et al applied the robust indirect ABSC(Adaptive Back-Stepping Control) scheme to EHA systems for variations of the viscous friction coefficient and effective bulk modulus (8) .…”

Section: Introductionmentioning

confidence: 99%

Robust Position Control of Electro-Hydrostatic Actuator Systems with Radial Basis Function Neural Networks

Choi

2013

JAMDSM

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This paper presents the robust tracking control of EHA (Electro Hydrostatic Actuator) system. EHA system provides the simple structure and frees from the environment pollution, but the poor tracking performance is caused by the internal leakage, surging of pump and friction. In order to increase the tracking performance of EHA system, ABSC (Adaptive Back-Stepping Control) with RBFNN (Radial Basis Function Neural Networks) is proposed. The proposed control scheme can guarantee the robustness and increase the tracking performance in accordance with the external disturbance and unmodeled uncertainties. The update law for the proposed ABSC with RBFNN is designed based on the reconstruction error. To verify the effectiveness of the proposed control scheme, computer simulation and experiments are executed and the results were compared with that of the general BSC(Back-Stepping Control).

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Failure monitoring in a high performance hydrostatic actuation system using the extended Kalman filter

Cited by 46 publications

References 13 publications

Single fault isolation of a Hydrostatic Transmission System: Simulation and Experimentation

Single fault isolation of a Hydrostatic Transmission System: Simulation and Experimentation

Using the Kalman filter for parameter estimation in biogeochemical models

Robust Position Control of Electro-Hydrostatic Actuator Systems with Radial Basis Function Neural Networks

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