Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as\ncruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator\nfailure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and\nfinancial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators.\nTherefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault\ndetection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation\n(MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis\nalgorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose\nthe fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF\nobservers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The\nresiduals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault\ntype can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that\nthe FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor.
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