Simulation of a Machine Learning Based Controller for a Fixed-Wing UAV with Distributed Sensors

Recent research suggests that the information obtained from arrays of sensors distributed on the wing of a fixed-wing small unmanned aerial vehicle (UAV) can provide information not available to conventional sensor suites. These arrays of sensors are capable of sensing the flow around the aircraft and it has been indicated that they could be a potential tool to improve flight control and overall flight performance. However, more work needs to be carried out to fully exploit the potential of these sensors for flight control. This work presents a 3 degrees-of-freedom longitudinal flight dynamics and control simulation model of a small fixed-wing UAV. Experimental readings of an array of pressure and strain sensors distributed across the wing were integrated in the model. This study investigated the feasibility of using machine learning to control airspeed of the UAV using the readings from the sensing array, and looked into the sensor layout and its effect on the performance of the controller. It was found that an artificial neural network was able to learn to mimic a conventional airspeed controller using only distributed sensor signals, but showed better performance for controlling changes in airspeed for a constant altitude than holding airspeed during changes in altitude. The neural network could control airspeed using either pressure or strain sensor information, but having both improved robustness to increased levels of turbulence. Results showed that some strain sensors and many pressure sensors signals were not necessary to achieve good controller performance, but that the pressure sensors near the leading edge of the wing were required. Future work will focus on replacing other elements of the flight control system with machine learning elements and investigate the use of reinforcement learning in place of supervised learning.</p

A wireless ultrasonic NDT sensor system

Ultrasonic condition monitoring technologies have been traditionally utilized in industrial and construction environments where structural integrity is of concern. Such techniques include active systems with either single or multiple transmit-receiver combinations used to obtain defect positioning and magnitude. Active sensors are implemented in two ways; in a thickness operation mode, or as an area-mapping tool operating over longer distances. In addition, passive ultrasonic receivers can be employed to detect and record acoustic emission activity. Existing equipment requires cabling for such systems leading to expensive, complicated installations. This work describes the development and operation of a system that combines these existing ultrasonic technologies with modern wireless techniques within a miniaturized, battery-operated design. A completely wireless sensor has been designed that can independently record and analyze ultrasonic signals. Integrated into the sensor are custom ultrasonic transducers, associated analogue drive and receive electronics, and a Texas Instruments Digital Signal Processor (DSP) used to both control the system and implement the signal processing routines. BlueTooth wireless communication is used for connection to a central observation station, from where network operation can be controlled. Extending battery life is of prime importance and the device employs several strategies to do this. Low voltage transducer excitation suffers from poor signal-to-noise ratios, which can be enhanced by signal processing routines implemented on the DSP. Routines investigated include averaging, digital filtering and pulse compression