Efficient Nonlinear Actuator Fault Detection and Isolation System for Unmanned Aerial Vehicles

2008 ◽  
Vol 31 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Guillaume Ducard ◽  
Hans P. Geering
Keyword(s):  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Isolation System ◽  
Aerial Vehicles

scholarly journals Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

2015 ◽  
Vol 25 (1) ◽  
pp. 189-201 ◽  
Author(s):  
Guillaume J.J. Ducard
Keyword(s):  
Fault Detection ◽  
Active Fault ◽  
Parameter Tuning ◽  
Fault Isolation ◽  
Unmanned Aircraft ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Single Model ◽  
Isolation System ◽  
Active Fault Detection

Abstract This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.

scholarly journals EKF-Based Actuator Fault Detection and Diagnosis Method for Tilt-Rotor Unmanned Aerial Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxin Gao ◽  
Qian Zhang ◽  
Jiyang Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Fault Detection And Diagnosis ◽  
Actuator Fault ◽  
Tilt Rotor ◽  
Aerial Vehicles ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Detection And Diagnosis

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.

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