Robust adaptive backstepping neural networks fault tolerant control for mobile manipulator UAV with multiple uncertainties

Samir Zeghlache, Hilal Rahali, Ali Djerioui, Loutfi Benyettou and Mohamed Fouad Benkhoris

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 218, issue C, 556-585

Abstract: The present study outlines the development of an Adaptive Backstepping Radial Basis Function Neural Networks Fault Tolerant Control (ABRBFNNFTC) methodology. The aforementioned methodology is employed to address the challenge of achieving trajectory following in the context of a Mobile Manipulator Unmanned Aerial Vehicle (MMUAV) when subjected to the effects of actuator faults and parametric uncertainties. The utilization of an adaptive radial basis function neural networks (RBFNNs) controller is employed for the purpose of approximating an unidentified nonlinear backstepping controller that relies on the precise model of the MMUAV. The Lyapunov direct method is utilized to establish the stability analysis of the entire system. The closed-loop system guarantees the Uniformly Ultimately Bounded (UUB) stability of all signals. The control methodology put forth ensures the achievement of a prescribed trajectory, and mitigates the impact of uncertainties and actuator faults. The efficiency of the proposed ABRBFNNFTC scheme is demonstrated through the presentation of extensive simulation studies.

Keywords: Unmanned aerial vehicle manipulator system; Radial basis function neural networks; Fault tolerant control; Backstepping control; Adaptive control (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:218:y:2024:i:c:p:556-585

DOI: 10.1016/j.matcom.2023.11.037

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