Abstract
Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as cruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator failure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and financial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators. Therefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault detection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation (MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis algorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose the fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF observers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The residuals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault type can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that the FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor.
Highlights
Tilt-rotor UAV, equipped with tilt-rotors, is a new type of UAV which can take off and land vertically on any fixed ground as well as cruise at high speed [1,2,3]. e tilt-rotor UAVs are being increasingly used in civil and military applications, such as the search and rescue operations, remote sensing, geographic studies, weather monitoring, and various military applications [4,5,6,7,8]
A sensor fault detection and diagnosis system for small autonomous helicopters was presented in [19]. e system used a Kalman filter observer, obtained from input-output experimental data, to detect fault by evaluating any significant change in the behavior of the vehicle. e system is implemented with real flight data, and the results demonstrate that “hard” and “soft” failures can be detected by the fault detection system, and the detection results are better and more robust when compared to the obtained using ARX linear observers
In [20], a fault detection and diagnosis strategy based on neural network and fuzzy system is proposed to detect sensor and actuator faults of UAV. e strategy was verified with a flight control system of UAV, and the results showed that this strategy could successfully detect the sensor faults
Summary
Tilt-rotor UAV, equipped with tilt-rotors, is a new type of UAV which can take off and land vertically on any fixed ground as well as cruise at high speed [1,2,3]. e tilt-rotor UAVs are being increasingly used in civil and military applications, such as the search and rescue operations, remote sensing, geographic studies, weather monitoring, and various military applications [4,5,6,7,8]. A two-stage Kalman filter was used to isolate and estimate possible faults, which were modeled as losses in the control effectiveness of rotors, in each actuator. Multiple-model adaptive estimation is a common method to detect actuator or sensor fault [21] It is composed of multiple Kalman filters, and each of them corresponds to the system state under a specific fault. E effectiveness of the proposed method is implemented and evaluated by simulations, and the simulation results show that the actuator FDD strategy could diagnose the actuator faults successfully with high levels of accuracy and efficiency without any need to add additional sensors to measure actuator deflections or to change the flight controller.
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