Adaptive Control of Variable-Pitch Propellers: Pursuing Minimum-Effort Operation

Travis Henderson, Nikolaos Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As Unmanned Aerial Vehicles (UAVs) become more commonly used in industry, their performance will continue to be challenged. A performance bottleneck that is crucial to overcome is the design of electric propulsion systems for UAVs that operate in disparate flight modes (e.g., hovering and forward-moving flight). While flight mode dissimilarity presents a fundamental design challenge for fixed-geometry propulsion systems, variable-geometry systems such as the Variable Pitch Propeller (VPP) ones are able to provide superior propulsion performance across a wide range of flight modes. This work builds on previous work by the authors and presents a VPP system control and estimation framework for safe, nearminimum-electrical-effort propulsion system behavior across the whole operation state space of any UAV. Multiple simulated validations are presented to support the feasibility of the approach.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7470-7476
Number of pages7
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/TerritoryFrance
CityParis
Period5/31/208/31/20

Bibliographical note

Funding Information:
VI. ACKNOWLEDGEMENTS The authors would like to thank all the members of the Center for Distributed Robotics Laboratory for their help. This material is based upon work partially supported by the Corn Growers Association of MN, the Minnesota Robotics Institute (MnRI), Honeywell, and the National Science Foundation through grants CNS-1432957, CNS-1544887, and CNS-1531330. USDA/NIFA has also supported this work through the NRI program.

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