Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload
Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/10/311 |
| _version_ | 1797473634290237440 |
|---|---|
| author | Özhan Bingöl Hacı Mehmet Güzey |
| author_facet | Özhan Bingöl Hacı Mehmet Güzey |
| author_sort | Özhan Bingöl |
| collection | DOAJ |
| description | Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller. |
| first_indexed | 2024-03-09T20:20:00Z |
| format | Article |
| id | doaj.art-0227941466014961ac2129061c773d32 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T20:20:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-0227941466014961ac2129061c773d322023-11-23T23:50:15ZengMDPI AGDrones2504-446X2022-10-0161031110.3390/drones6100311Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended PayloadÖzhan Bingöl0Hacı Mehmet Güzey1Department of Electrical and Electronics Engineering, Gumushane University, Baglarbası Str., 29100 Gümüşhane, TurkeyDepartment of Electrical and Electronics Engineering, Sivas University of Science and Technology, Kardesler Str. No: 7/1, 58100 Sivas, TurkeyDue to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller.https://www.mdpi.com/2504-446X/6/10/311finite-time stabilityneural networkquadrotor UAVsliding mode control |
| spellingShingle | Özhan Bingöl Hacı Mehmet Güzey Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload Drones finite-time stability neural network quadrotor UAV sliding mode control |
| title | Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload |
| title_full | Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload |
| title_fullStr | Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload |
| title_full_unstemmed | Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload |
| title_short | Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload |
| title_sort | finite time neuro sliding mode controller design for quadrotor uavs carrying suspended payload |
| topic | finite-time stability neural network quadrotor UAV sliding mode control |
| url | https://www.mdpi.com/2504-446X/6/10/311 |
| work_keys_str_mv | AT ozhanbingol finitetimeneuroslidingmodecontrollerdesignforquadrotoruavscarryingsuspendedpayload AT hacımehmetguzey finitetimeneuroslidingmodecontrollerdesignforquadrotoruavscarryingsuspendedpayload |