Abstract
Reliability is a critical issue in navigation of unmanned aerial vehicles (UAVs) since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical‐grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault‐free behaviour, which is estimated by using an observer. The observer is obtained from input‐output experimental data with the Observer/Kalman Filter Identification (OKID) method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input‐output experimental data. Results are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.
Highlights
Unmanned aerial vehicles UAVs are increasingly used in many applications in which ground vehicles cannot access to the desired locations due to the characteristics of the terrain and the presence of obstacles
This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy
The results of MARVIN sensor FDI system using Kalman filters obtained with the OKID method are presented
Summary
Unmanned aerial vehicles UAVs are increasingly used in many applications in which ground vehicles cannot access to the desired locations due to the characteristics of the terrain and the presence of obstacles. Fixed wing UAVs, rotorcrafts, and airships with different characteristics have been proposed and experimented see, e.g., 1. Helicopters have high manoeuvrability and hovering ability. They are well suited to agile target tracking tasks, as well as to inspection. The vertical takeoff and landing of helicopters is very desirable in many applications. Piloted helicopters are inherently unstable and dynamically fast. Even with improved stability augmentation devices, a skilled, experienced pilot is required to control them during flight. Autonomous helicopter control is a challenging task involving a multivariable nonlinear open-loop unstable system with actuator saturations
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