عنوان مقاله [English]
In this paper, a new model-based fault detection method for an agile supersonic
flight vehicle is presented. A nonlinear model, controlled by a classical closed loop controller and proportional navigation guidance in interception scenario, describes the behavior of the vehicle. The proposed FDD method uses Inertial measurement unit (IMU) data and nonlinear dynamic model of the vehicle to inform fins damage to the controller before leading to an undesired performance or mission failure. Broken, burnt or not opening of control surfaces causes a drastic change in aerodynamic coefficients and thus dynamic model. Therefore, in addition to the changes in the control forces and moments, system dynamics will change too, leading to the failure detection process being encountered with difficulty. To this purpose, an equivalent aerodynamic model is proposed to represent the dynamics of the vehicle, and the health of each fin is monitored by the value of a parameter which is estimated using a nonlinear filter. To identify model changes after fin failure, the aerodynamics of body and control surfaces are modeled separately. A percentage of the fins which have the ability to generate aerodynamic force is modeled by a parameter and these parameters are estimated over time, using a nonlinear estimator. Parameters estimation through the filtering approach is an indirect procedure, consisting of transforming the problem into a state estimation problem. The value of these parameters will be between 0 and 1. Where value 1 corresponds to the health of the fin and value denotes whether the fin has not opened or has been destroyed completely. The proposed method detects and isolates fins damage in a few seconds with good accuracy. Simulation results show that the new failure diagnosis algorithm estimated fins health percentage with good accuracy. After estimating the parameters, they can be sent to the controller and the controller has changed control signal.