Titre :

Robust trajectory tracking of quadrotors using adaptive radial basis function network compensation control

Auteur(s) :

Bouaiss, Oussama [Auteur]
University of Biskra Mohamed Khider
Mechgoug, Raihane [Auteur]
University of Biskra Mohamed Khider
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Brikel, Ala Eddine [Auteur]
University of Biskra Mohamed Khider

Titre de la revue :

Journal of The Franklin Institute

Pagination :

1167-1185

Éditeur :

Elsevier

Date de publication :

2024-02

ISSN :

0016-0032

Discipline(s) HAL :

Physique [physics]
Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive ...
Lire la suite >Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive RBF compensation and NN-supervised control embedded with Integrator BackStepping (IBS). The approach addresses the robustness in the presence of modeling uncertainties, sensing noise, and bounded disturbances. The control design is derived from the decentralized inverse dynamics, using adaptive RBFNN for outer-loop disturbance approximation and compensation. In conjunction with an Inner-loop supervised control that stabilizes the quadrotor attitude, preventing initial instability during NN convergence. In addition, an adaptive Extended Kalman Filter (EKF) attenuates noisy signals. Simulation results demonstrate strong adaptability to changes in model parameters, and superior performance when compared to Proportional Integral Derivative (PID), Integrator BackStepping (IBS), and offline decentralized Multi-Layer Perceptron (MLP) algorithms, in terms of parameter convergence, disturbance compensation control, and noise attenuation.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL