Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine
AbstractIn this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a “reconstruction signal” to estimate health parameters modeled as artificial inputs, and is not only applicable to long-time health degradation, but reacts much quicker in handling abrupt fault cases. In view of the inevitable uncertainties in engine dynamics and modeling, a weighting matrix is created to minimize such effect on estimation by using the linear matrix inequalities (LMI). A big step toward uncertainty modeling is taken compared with our previous SMO-based work, in that uncertainties are considered in a more practical form. Moreover, to avoid chattering in sliding modes, the super-twisting algorithm (STA) is employed in observer design. Various simulations are carried out, based on the comparisons between the KF-based scheme, the SMO-based scheme in our earlier research, and the proposed method. The results consistently demonstrate the capabilities and advantages of the proposed approach in health parameter estimation. View Full-Text
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Chang, X.; Huang, J.; Lu, F. Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine. Energies 2017, 10, 1040.
Chang X, Huang J, Lu F. Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine. Energies. 2017; 10(7):1040.Chicago/Turabian Style
Chang, Xiaodong; Huang, Jinquan; Lu, Feng. 2017. "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine." Energies 10, no. 7: 1040.
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