Mantas Brazinskas - University of Southampton
Stephen D. Prior - University of Southampton
James P. Scanlan - University of Southampton
The interest and market for small multi-rotor unmanned aerial vehicles (UAVs) is growing rapidly. Both the civilian and military sectors use these platforms. Civilian applications include fire control, search and rescue, agriculture, maintenance of structures, and media. The military uses these platforms for ground control operations, border surveillance, crowd control and, in some cases, attack operations. When addressing multi-rotor systems, the system volume, payload capability, and endurance are the most important aspects. All these areas are closely linked to the platform propulsion system.
Overlapping rotor propulsion systems provide one of the smallest platform volume per thrust output; however, this comes at a cost to the efficiency when compared to fully isolated rotor systems. Most research into full-sized helicopter overlapping propulsion systems involves coaxial setups (fully overlapped). Partially overlapping rotor setups (tandem multi-rotor) have received less attention, with little empirical data produced over the years. The increase in demand for compact small unmanned aircraft has exposed the need for empirical investigations of overlapping propulsion systems at a small scale (Reynolds Number <250,000).
University of Southampton research programs have a variety of topics that require empirical data to support theoretical investigations. Aeronautics, astronautics, and computational engineering department research projects are no exception. We decided to develop a system to quantify rotor-to-rotor interference at small UAV scale and build up an empirical data library.
The Test Rig
The operator can use the test rig mechanical structure to manipulate overlapping propulsion variables (see Figure 1 and Figure 2). The system also integrated control, data acquisition, and data processing. These features make it possible to investigate small UAV rotor-to-rotor interference effects.
We achieved the desired data capture rates with ease using the cRIO-9064 embedded controller with integrated Artix-7 FPGA module. We programmed the CompactRIO controller with LabVIEW to acquire real-time data from 12 individual sensors while providing control inputs for the tested setup. We used Direct Memory Access (DMA) first in first out (FIFO),DA defined in LabVIEW, to process and store data, as well as transfer control set points from host to the FPGA integrated into the CompactRIO chassis.
Acquiring data to map the performance of the overlapping propulsion system requires measurements from six sensors per rotor. We used full bridge strain sensors to capture rotor torque and thrust. To decrease integration time, we used a dedicated analogue input module, the NI-9237. This module can provide excitation voltages to the sensors, so it eliminated the need for external circuitry or power supplies, which could potentially induce electrical noise. We have acquired electrical potential and current with the NI-9215 analogue input modules. We used a NI-9401 high-speed digital I/O module to obtain the rotor velocity and generate the speed control signal. Figure 3 shows the full test rig system diagram, and Figure 4 shows the CompactRIO.
We monitored all the test rig sensors simultaneously, which is inherently feasible by using the FPGA, but also conveniently supported by the C Series I/O modules. We passed the acquired sensor data (transferred from FPGA to host) and control set points (transferred from host to FPGA) using built-in DMA FIFO libraries, which were extremely easy to use and did not require much effort to set up needed reliable data routes. We implemented the control of test rig variables such as thrust, torque, and rotor speed using the LabVIEW PID and Fuzzy Logic Toolkit. Because we did not need to design our own PID controllers, we saved time developing the control architecture for the test rig.
We chose NI hardware and software because of the compatibility with the sensors used, ease of initial setup, and support from the NI team. The NI platform also offers the greatest flexibility for future equipment developments because we can easily transfer both code modules and I/O modules to future test equipment. Training courses in LabVIEW and Embedded Control and Monitoring helped kick start our development process of the test rig. The many tutorials and online trainings have addressed most of the challenges that we faced along the way. When we needed additional help, NI application engineers offered in-depth support.
To conclude, NI delivered excellent support throughout the development of the test rig software. High-quality results were imperative to our research, but LabVIEW and CompactRIO exceeded our expectations. Even when we were getting started, the NI platform helped us quickly integrate complex control and DAQ methods, which greatly accelerated the development of our automated test rig for the assessment of rotor-to-rotor interference in small UAVs. The value of NI’s products and the amount of time saved was irreplaceable.
The produced test rig has already proven to be useful in mapping the performance of an overlapping propulsion system when using off-the-shelf UAV rotors (Figure 6). This data represents a snapshot of what we achieved with the high-quality reliable NI equipment in a short time period. We can now use such data to correct theoretical calculations, which allows us to carry out more complex investigations and will ultimately increase the efficiency of future small multi-rotor systems.
We plan to publish the data library in a PhD thesis, but we already use it to support propulsion system analytical calculations, such as blade element momentum theory (BEMT). After the data library is released, other engineers can save a huge amount of time investigating rotor-to-rotor effects, which will ultimately accelerate the development of future small UAVs.
University of Southampton