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  4. High-fidelity Model and Nonlinear Model Predictive Control for Flip Maneuvers of Tailless Flapping-Wing Robots
 
conference paper

High-fidelity Model and Nonlinear Model Predictive Control for Flip Maneuvers of Tailless Flapping-Wing Robots

Guo, Qingcheng
•
Wu, Chaofeng
•
Lu, Jun-Guo
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October 19, 2025
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Insects and hummingbirds exhibit remarkable agility, including full body flip maneuvers. Achieving similar maneuvers of bio-inspired tailless flapping-wing robots (FWRs) is challenging due to the complex dynamics, inherent nonlinearities and control issues. This paper presents an nonlinear model predictive control (NMPC) algorithm to enable the 360-degree flip maneuver for the developed X-wing tailless FWR, which weighs 30.8 g and has a wingspan of 14.5 cm. We first introduce a high-fidelity model of the FWR, which incorporates the aerodynamics of the wings, dynamics of the motors and servos, body kinodynamic model, and the model of thrust and torques generation. This high-fidelity model allows for testing the FWR in simulation environments, thereby reducing the damage and cost associated with flip maneuvers in real-world experiments. Based on this high-fidelity model, we propose an NMPC controller to offline compute optimal state trajectories and corresponding control inputs, which are then used as state references and the feedforward control for the FWR during its 360-degree flip maneuvers. Next, we present an online basic feedback controller that integrates the feedforward control for the FWR’s flip control. Experimental results demonstrate the successful execution of the flip maneuvers without any mechanical modifications, highlighting the effectiveness of the proposed control strategy.

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Type
conference paper
DOI
10.1109/iros60139.2025.11245981
Author(s)
Guo, Qingcheng
Wu, Chaofeng
Lu, Jun-Guo
Hughes, Josie  

École Polytechnique Fédérale de Lausanne

Date Issued

2025-10-19

Publisher

IEEE

Published in
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI of the book
https://doi.org/10.1109/IROS60139.2025
ISBN of the book

979-8-3315-4393-8

Start page

15759

End page

15765

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CREATE-LAB  
Event nameEvent acronymEvent placeEvent date
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

IROS 2025

Hangzhou, China

2025-10-19 - 2025-10-25

FunderFunding(s)Grant NumberGrant URL

Ministry of Education

Available on Infoscience
December 1, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/256513
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