Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle
This paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is pr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/12/727 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850059026110349312 |
|---|---|
| author | Guillaume Ducard Gregorio Carughi |
| author_facet | Guillaume Ducard Gregorio Carughi |
| author_sort | Guillaume Ducard |
| collection | DOAJ |
| description | This paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is presented. An “imitative learning” approach is employed to train the NNC to mimic the response of an expert but computationally expensive model predictive controller (MPC). The resulting NNC approximates the MPC’s solution while significantly decreasing the computational cost. The NNC is trained on the longitudinal axis. Successful simulations and real flight tests prove that the NNC is suitable for the longitudinal axis control of a complex nonlinear system such as the tilt-rotor VTOL UAV through a sequence of transitions between the RW mode to the FW mode, and vice versa, in a forward flight. |
| format | Article |
| id | doaj-art-fe1665f74f20413796fd09ecba7a4cd8 |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-fe1665f74f20413796fd09ecba7a4cd82025-08-20T02:50:59ZengMDPI AGDrones2504-446X2024-12-0181272710.3390/drones8120727Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial VehicleGuillaume Ducard0Gregorio Carughi1Laboratoire d’Informatique, Signaux et Systèmes de Sophia Antipolis, Université Côte d’Azur, 06903 Nice, FranceInstitute for Dynamics, Systems and Control (IDSC), ETH Zürich, 8092 Zürich, SwitzerlandThis paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is presented. An “imitative learning” approach is employed to train the NNC to mimic the response of an expert but computationally expensive model predictive controller (MPC). The resulting NNC approximates the MPC’s solution while significantly decreasing the computational cost. The NNC is trained on the longitudinal axis. Successful simulations and real flight tests prove that the NNC is suitable for the longitudinal axis control of a complex nonlinear system such as the tilt-rotor VTOL UAV through a sequence of transitions between the RW mode to the FW mode, and vice versa, in a forward flight.https://www.mdpi.com/2504-446X/8/12/727machine learningimitative learningneural network-based controlunified flight controltilt-rotor VTOL UAVconvertible VTOL UAV |
| spellingShingle | Guillaume Ducard Gregorio Carughi Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle Drones machine learning imitative learning neural network-based control unified flight control tilt-rotor VTOL UAV convertible VTOL UAV |
| title | Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle |
| title_full | Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle |
| title_fullStr | Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle |
| title_full_unstemmed | Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle |
| title_short | Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle |
| title_sort | neural network design and training for longitudinal flight control of a tilt rotor hybrid vertical takeoff and landing unmanned aerial vehicle |
| topic | machine learning imitative learning neural network-based control unified flight control tilt-rotor VTOL UAV convertible VTOL UAV |
| url | https://www.mdpi.com/2504-446X/8/12/727 |
| work_keys_str_mv | AT guillaumeducard neuralnetworkdesignandtrainingforlongitudinalflightcontrolofatiltrotorhybridverticaltakeoffandlandingunmannedaerialvehicle AT gregoriocarughi neuralnetworkdesignandtrainingforlongitudinalflightcontrolofatiltrotorhybridverticaltakeoffandlandingunmannedaerialvehicle |