Describing function analysis of second order sliding mode observers

2006 ◽  
Author(s):  
I. Boiko ◽  
I. Castellanos ◽  
L. Fridman
Keyword(s):  
Sliding Mode ◽  
Function Analysis ◽  
Second Order ◽  
Describing Function ◽  
Sliding Mode Observers ◽  
Second Order Sliding Mode

scholarly journals Second-order sliding mode observers for fault reconstruction in power networks

2017 ◽  
Vol 11 (16) ◽  
pp. 2772-2782 ◽  
Author(s):  
Chiara Mellucci ◽  
Prathyush P. Menon ◽  
Christopher Edwards ◽  
Antonella Ferrara
Keyword(s):  
Sliding Mode ◽  
Second Order ◽  
Power Networks ◽  
Fault Reconstruction ◽  
Sliding Mode Observers ◽  
Second Order Sliding Mode

scholarly journals Robust Sensor Fault Reconstruction via a Bank of Second-Order Sliding Mode Observers for Aircraft Engines

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2831
Author(s):  
Zijian Qiang ◽  
Jinquan Huang ◽  
Feng Lu ◽  
Xiaodong Chang
Keyword(s):  
Robust Design ◽  
Sliding Mode ◽  
Numerical Study ◽  
Second Order ◽  
Aircraft Engines ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Fault Reconstruction ◽  
Sliding Mode Observers ◽  
Second Order Sliding Mode

This paper deals with sensor faults of aircraft engines under uncertainties using a bank of second-order sliding mode observers (SMOs). In view of the effect of inevitable uncertainties on the fault reconstruction, a method combining H ∞ concepts and linear matrix inequalities (LMIs) is proposed, in which a scaling matrix is designed to minimize the gain of the transfer function matrix from uncertainty to reconstruction. However, robust design generally requires that engine outputs outnumber faults. In the case where the above-mentioned requirement is not satisfied, a bank of sliding mode observers is proposed to ensure the degrees of freedom available in robust design. In specific, each observer corresponds to a certain sensor with the hypothesis that the corresponding sensor will not have faults, to create one degree of design freedom for each observer. After fault occurrence, a large estimation error is expected in the observers with wrong hypothesis, and then a logic module is designed to detect sensor faults and obtain the optimal robust sensor fault reconstruction at the same time. The proposed approach is applied to a nonlinear engine component-level-model (CLM) simulation platform, and a numerical study is performed to validate the effectiveness.

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