Several procedures for sensor fault detection and isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main contributions of the paper are related to the design and the optimisation of two FDI schemes based on a linear polynomial method (PM) and the nonlinear geometric approach (NLGA). The FDI strategies are applied to the aircraft model, characterised by tight-coupled longitudinal and lateral dynamics. The robustness and the reliability properties of the residual generators related to the considered FDI techniques are investigated and verified by simulating a general aircraft reference trajectory. Extensive simulations exploiting the Monte Carlo analysis tool are also used for assessing the overall performance capabilities of the developed FDI schemes, in the presence of turbulence, measurement, and model errors. Comparisons with other disturbancedecoupling methods for FDI based on neural networks (NNs) and unknown input kalman filter (UIKF) are finally reported.
Design and Analysis of Robust Fault Diagnosis Schemes for a Simulated Aircraft Model
CASTALDI, PAOLO;GERI, WALTER;
2008
Abstract
Several procedures for sensor fault detection and isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main contributions of the paper are related to the design and the optimisation of two FDI schemes based on a linear polynomial method (PM) and the nonlinear geometric approach (NLGA). The FDI strategies are applied to the aircraft model, characterised by tight-coupled longitudinal and lateral dynamics. The robustness and the reliability properties of the residual generators related to the considered FDI techniques are investigated and verified by simulating a general aircraft reference trajectory. Extensive simulations exploiting the Monte Carlo analysis tool are also used for assessing the overall performance capabilities of the developed FDI schemes, in the presence of turbulence, measurement, and model errors. Comparisons with other disturbancedecoupling methods for FDI based on neural networks (NNs) and unknown input kalman filter (UIKF) are finally reported.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
