Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

Wonkeun Youn; Hanseok Ryu; Dongjin Jang; Changho Lee; Youngmin Park; Dongjin Lee; Matthew B. Rhudy

doi:10.1109/LRA.2021.3086428

Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

Wonkeun Youn, Hanseok Ryu, Dongjin Jang, Changho Lee, Youngmin Park, Dongjin Lee, Matthew B. Rhudy

Division of Engineering, Business & Computing (Berks)

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

Original language	English (US)
Article number	9447232
Pages (from-to)	5841-5848
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	6
Issue number	3
DOIs	https://doi.org/10.1109/LRA.2021.3086428
State	Published - Jul 2021

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

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10.1109/LRA.2021.3086428

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title = "Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy",

abstract = "This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy. ",

author = "Wonkeun Youn and Hanseok Ryu and Dongjin Jang and Changho Lee and Youngmin Park and Dongjin Lee and Rhudy, {Matthew B.}",

note = "Funding Information: Manuscript received January 14, 2021; accepted May 11, 2021. Date of publication June 4, 2021; date of current version June 16, 2021. This letter was recommended for publication by Associate Editor Moses Bangura and Editor Pauline Pounds upon evaluation of the reviewers{\textquoteright} comments. This work was supported in part by the “Development of Certification Technology for Small Unmanned Aerial Vehicle Systems” program through the Korea Agency for Infrastructure Technology Advancement (KAIA), funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) under Grant 21CAUV-B151724-03, and in part by the research fund of Chungnam National University. (Corresponding authors: Matthew B. Rhudy and Dongjin Lee.) Wonkeun Youn is with the Department of Autonomous Vehicle System Engineering, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea (e-mail: wkyoun@cnu.ac.kr). Publisher Copyright: {\textcopyright} 2016 IEEE.",

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AU - Park, Youngmin

AU - Lee, Dongjin

AU - Rhudy, Matthew B.

N1 - Funding Information: Manuscript received January 14, 2021; accepted May 11, 2021. Date of publication June 4, 2021; date of current version June 16, 2021. This letter was recommended for publication by Associate Editor Moses Bangura and Editor Pauline Pounds upon evaluation of the reviewers’ comments. This work was supported in part by the “Development of Certification Technology for Small Unmanned Aerial Vehicle Systems” program through the Korea Agency for Infrastructure Technology Advancement (KAIA), funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) under Grant 21CAUV-B151724-03, and in part by the research fund of Chungnam National University. (Corresponding authors: Matthew B. Rhudy and Dongjin Lee.) Wonkeun Youn is with the Department of Autonomous Vehicle System Engineering, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea (e-mail: wkyoun@cnu.ac.kr). Publisher Copyright: © 2016 IEEE.

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N2 - This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

AB - This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

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Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

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