We demonstrate a dynamic distributed monitoring technique using a long-length fiber Bragg grating (FBG) interrogated by optical frequency domain reflectometry (OFDR) that measures strain at a speed of 150 Hz, spatial resolution of 1 mm, and measurement range of 20 m. A 5 m FBG is bonded to a 5.5 m helicopter blade model, and vibration is applied by the step relaxation method. The time domain responses of the strain distributions are measured, and the blade deflections are calculated based on the strain distributions. Frequency response functions are obtained using the time domain responses of the calculated deflection induced by the preload release, and the modal parameters are retrieved. Experimental results demonstrated the dynamic monitoring performances and the applicability to the modal analysis of the OFDR-FBG technique.

We have developed an optical fiber distributed sensing system based on optical frequency domain reflectometry (OFDR) that uses long-length fiber Bragg gratings (FBGs). This technique obtains strain data not as a point data from an FBG but as a distributed profile within the FBG. This system can measure the strain distribution profile with an adjustable high spatial resolution of the mm or sub-mm order in real-time. In this study, we applied this OFDR-FBG technique to a flying test bed that is a mid-sized jet passenger aircraft. We conducted flight tests and monitored the structural responses of a fuselage stringer and the bulkhead of the flying test bed during flights. The strain distribution variations were successfully monitored for various events including taxiing, takeoff, landing and several other maneuvers. The monitoring was effective not only for measuring the strain amplitude applied to the individual structural parts but also for understanding the characteristics of the structural responses in accordance with the flight maneuvers. We studied the correlations between various maneuvers and strains to explore the relationship between the operation and condition of aircraft.

We conduct flight demonstrations of aircraft monitoring by using optical fiber distributed sensing technique. We monitor a main wing of a flying test bed that is a middle-sized passenger jet. We use optical frequency domain reflectometry and long-length fiber Bragg gratings (FBGs). The sensing system measures the strain distribution profile within the FBGs with a mm order spatial resolution in real-time. Thanks to the high spatial resolution, we could observe local strain distributions due to the ribs of the wing. We also monitored strain distribution variations corresponding to various maneuvers during flight. We discussed the correlation with flight maneuvers and the interpretation of the monitoring data.

Deep reinforcement learning has great potential to automatically generate flight controllers for uncrewed aerial vehicles (UAVs), however these controllers often fail to perform as expected in real world environments due to differences between the simulation environment and reality. This letter experimentally investigated how this reality gap effect could be mitigated, focusing on fixed-wing UAV pitch control in wind tunnel tests. Three different training approaches were conducted: a baseline approach that used simple linear dynamics, a high-fidelity modeling approach, and a domain randomization approach. It was found that the base line controller was susceptible to the reality gap, while the other two approaches successfully transferred to real tests. To further examine the controllers' capabilities to generalize, a variety of configuration changes were experimentally implemented on the UAV, such as increased inertia, extended elevator area, and aileron offset. While the high-fidelity controller failed to cope with these changes, the controller with domain randomization maintained its performance. These results highlight the importance of selecting appropriate sim-to-real transfer approaches and how domain randomization is applicable to fixed-wing UAV control with uncertainty in real environments.

Nonlinear flight controllers for fixed-wing unmanned aerial vehicles (UAVs) can potentially be developed using deep reinforcement learning. However, there is often a reality gap between the simulation models used to train these controllers and the real world. This study experimentally investigated the application of deep reinforcement learning to the pitch control of a UAV in wind tunnel tests, with a particular focus of investigating the effect of time delays on flight controller performance. Multiple neural networks were trained in simulation with different assumed time delays and then wind tunnel tested. The neural networks trained with shorter delays tended to be susceptible to delay in the real tests and produce fluctuating behaviour. The neural networks trained with longer delays behaved more conservatively and did not produce oscillations but suffered steady state errors under some conditions due to unmodeled frictional effects. These results highlight the importance of performing physical experiments to validate controller performance and how the training approach used with reinforcement learning needs to be robust to reality gaps between simulation and the real world.