Neuro-adaptive singularity-free finite-time attitude tracking control of quadrotor UAVs. (December 2021)

• Record Type: Journal Article
• Authors: He, Xiongxiong; Tao, Meiling; Xie, Shuzong; Chen, Qiang
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Adaptive control -- Finite-time control -- Neural network -- Quadrotors -- Singularity-free sliding mode surface; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107485
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, a neuro-adaptive finite-time control law is proposed for the quadrotor unmanned aerial vehicles. Instead of employing any piecewise continuous functions, a singularity-free terminal sliding mode surface and an auxiliary function are presented to overcome the singularity issues resulted from the differentiation of the sliding variable and the error-related inverse matrix in the controller design, respectively. A simple neural network is employed to estimate the unknown dynamics, such that no prior knowledge on system model uncertainties is required for designing attitude controllers. With the presented control law, the finite-time convergence of the attitude and angular velocity errors can be guaranteed by rigorously theoretical analysis, and numerical simulations are performed to demonstrate the satisfactory performance of the proposed method. Graphical abstract: Highlights: A new singularity-free sliding mode surface without piecewise continuous functions. An auxiliary function is designed to solve the hidden singularity. A controller is proposed to achieve the finite-time convergence of tracking errors. No prior knowledge about the unknown nonlinear uncertainties is required.