Given the wide variety of flight conditions typically encountered by fixed-wing aerial vehicles, the flight performance of a solar-powered unmanned aerial vehicle (SUAV) depends on many factors. Predicting the performance for a given application requires characterization of both system and environmental components. Furthermore, a SUAV with a transformable airframe increases the number of characterizable states, where each state features a unique set of capabilities. This results in significant differences with respect to power consumption, solar panel orientation, and increases the design limitations on system components. This paper characterizes the energy collection, propulsion, and power electronics subsystems of a transformable SUAV developed at the University of Minnesota. Clear-sky and solar panel models are used to predict the power available for a given location and time. Propulsion system design is validated with flight data, and a proposed model correlates propulsion limits with available energy. Power electronics are modeled and simulated to determine hardware and tracking algorithm efficiencies. Finally, a pulsed battery charging methodology is implemented in hardware and evaluated against conventional charging techniques.
|Original language||English (US)|
|Title of host publication||IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - Dec 13 2017|
|Event||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada|
Duration: Sep 24 2017 → Sep 28 2017
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Other||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017|
|Period||9/24/17 → 9/28/17|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #IIS-1017344, #IIP-1332133, #IIS-1427014, #IIP-1432957, #OISE-1551059, #CNS-1514626, #CNS-1531330, and #CNS-1544887. Ruben D’Sa was supported by a National Science Foundation Graduate Research Fellowship No. 00039202.