Section 5 presents the experimental data and the obtained results. Finally, the conclusions and future trends mentioned in Section 6 complete sellekchem the paper.2.?Sensor Inhibitors,Modulators,Libraries FDI in Small Autonomous HelicoptersSeveral small autonomous helicopter prototypes have been developed in recent years at different research centers throughout the world [1]. Autonomous helicopter flight requires precise position and attitude information for control and stabilization. Inhibitors,Modulators,Libraries Small autonomous helicopters carry a pack of sensors that in a typical case includes an inertial measurement unit (IMU) with three gyros, three accelerometers and a 3-axis magnetometer for attitude determination, a centimeter-precision kinematic DGPS and an ultrasonic or barometric altitude sensor for take-off and landing.
A fault in one of the sensors, if undetected, may induce position and Inhibitors,Modulators,Libraries attitude estimation errors. Reconfiguration in these cases usually consists in isolating the faulty sensor and using the other sensors to get the best estimation of position and attitude.2.1. Inertial, magnetometer and altitude sensor faultsUsual UAV sensor faults are additive (sensor output has a drift term added), multiplicative (sensor output is scaled by a multiplicative term) or ��stuck�� output (sensor output remains fixed at a constant value). The presence of these faults can be detected in most cases by means of the so-called residuals, i.e., quantities that are over-sensitive to the malfunctions. Observer-based and parameter estimation are the most frequently applied methods for residual generation in FDI [9].
Most published work in recent years on FDI systems for autonomous vehicles also use observer-based methods. Neural networks have also been used to detect sensor and actuator faults in aircrafts [10] and UAVs [4,11].The FDI system Inhibitors,Modulators,Libraries implemented in individual helicopters is described in detail in [5]. The structure of the sensor FDI Batimastat system is based on a bank of output estimators as shown in Figure 1. The number of these estimators is equal to the number of system outputs. A residual is generated for each sensor, comparing the estimator output with the sensor output. Each residual is not affected by the other sensors, and therefore fault identification is straightforward: each residual is only sensitive to a single helicopter sensor.Figure 1.Bank of estimators for output residual generation.
The FDI system with the above structure has been implemented using ARX input-output estimators. click this A number of ARX Multi-Input Single-Output (MISO) models have been identified from input-output data. These models are of the type:yi*(t)=��j=1n��i,j yi*(t?j)+��j=1r��k=1n��i,j,kuj*(t?k)+��i(t)(1)The number of identified MISO ARX models is equal to the number m of the output variables. The model order n and the parameters ��i,j an? ��i,j,k with i = 1, ��, m, of the model have to be determined by the identification approach. The term ��i(t) takes into account the modeling error, which is due to process noises, parameter variations, etc.