Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor

Walid Kh Alqaisi; Brahim Brahmi; Jawhar Ghommam; Maarouf Saad; Vahe Nerguizian

doi:10.1109/ROSE.2019.8790423

Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor

Walid Kh Alqaisi
, Brahim Brahmi
, Jawhar Ghommam
, Maarouf Saad
, Vahe Nerguizian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

This paper addresses the design of a robust adaptive sliding mode tracking control approach utilizing a Radial Basis Function Neural Network RBF NN for quadrotor. The proposed system has great advantages in dealing with nonlinearities and it has the ability to approximate uncertainties. The output of the neural network is used as a compensator parameter in order to eliminate system uncertainties. Consequently, fast error convergence in the closed loop control system can be achieved. A preliminary study to apply the system in an agricultural application using visual sensing is introduced and tested. The proposed system stability is proved by Lyapunov analysis, simulation and experimental implementation.

Original language	English
Title of host publication	IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728119649
DOIs	https://doi.org/10.1109/ROSE.2019.8790423
State	Published - Jun 2019
Externally published	Yes
Event	13th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Ottawa, Canada Duration: 17 Jun 2019 → 18 Jun 2019

Publication series

Name	IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings

Conference

Conference	13th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019
Country/Territory	Canada
City	Ottawa
Period	17/06/19 → 18/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Adaptive control
Quadrotor
RBF neural network
Sliding-mode
UAV

ASJC Scopus subject areas

Artificial Intelligence
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ROSE.2019.8790423

Cite this

Alqaisi, W. K., Brahmi, B., Ghommam, J., Saad, M., & Nerguizian, V. (2019). Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor. In IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings Article 8790423 (IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROSE.2019.8790423

Alqaisi, Walid Kh ; Brahmi, Brahim ; Ghommam, Jawhar et al. / Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor. IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings).

@inproceedings{a66d5282dbbc442b857afcbe46b27412,

title = "Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor",

abstract = "This paper addresses the design of a robust adaptive sliding mode tracking control approach utilizing a Radial Basis Function Neural Network RBF NN for quadrotor. The proposed system has great advantages in dealing with nonlinearities and it has the ability to approximate uncertainties. The output of the neural network is used as a compensator parameter in order to eliminate system uncertainties. Consequently, fast error convergence in the closed loop control system can be achieved. A preliminary study to apply the system in an agricultural application using visual sensing is introduced and tested. The proposed system stability is proved by Lyapunov analysis, simulation and experimental implementation.",

keywords = "Adaptive control, Quadrotor, RBF neural network, Sliding-mode, UAV",

author = "Alqaisi, \{Walid Kh\} and Brahim Brahmi and Jawhar Ghommam and Maarouf Saad and Vahe Nerguizian",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 13th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 ; Conference date: 17-06-2019 Through 18-06-2019",

year = "2019",

month = jun,

doi = "10.1109/ROSE.2019.8790423",

language = "English",

series = "IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings",

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Alqaisi, WK, Brahmi, B, Ghommam, J, Saad, M & Nerguizian, V 2019, Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor. in IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings., 8790423, IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 13th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019, Ottawa, Canada, 17/06/19. https://doi.org/10.1109/ROSE.2019.8790423

Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor. / Alqaisi, Walid Kh; Brahmi, Brahim; Ghommam, Jawhar et al.
IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8790423 (IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nerguizian, Vahe

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N2 - This paper addresses the design of a robust adaptive sliding mode tracking control approach utilizing a Radial Basis Function Neural Network RBF NN for quadrotor. The proposed system has great advantages in dealing with nonlinearities and it has the ability to approximate uncertainties. The output of the neural network is used as a compensator parameter in order to eliminate system uncertainties. Consequently, fast error convergence in the closed loop control system can be achieved. A preliminary study to apply the system in an agricultural application using visual sensing is introduced and tested. The proposed system stability is proved by Lyapunov analysis, simulation and experimental implementation.

AB - This paper addresses the design of a robust adaptive sliding mode tracking control approach utilizing a Radial Basis Function Neural Network RBF NN for quadrotor. The proposed system has great advantages in dealing with nonlinearities and it has the ability to approximate uncertainties. The output of the neural network is used as a compensator parameter in order to eliminate system uncertainties. Consequently, fast error convergence in the closed loop control system can be achieved. A preliminary study to apply the system in an agricultural application using visual sensing is introduced and tested. The proposed system stability is proved by Lyapunov analysis, simulation and experimental implementation.

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KW - Quadrotor

KW - RBF neural network

KW - Sliding-mode

KW - UAV

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Alqaisi WK, Brahmi B, Ghommam J, Saad M, Nerguizian V. Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor. In IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8790423. (IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings). doi: 10.1109/ROSE.2019.8790423

Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this