Dynamic event-triggered adaptive neural nonsingular fixed-time attitude control for multi-UAVs systems

Abstract

This article looks into the dynamic event-triggered fixed-time adaptive attitude control problem for nonlinear six-rotor unmanned aerial vehicle (UAV) with external disturbances. The multiple six-rotor UAVs considered are regarded as nonlinear multi-agent systems (MASs), and each subsystem has multiple inputs. Under the framework of backstepping recursive design, an effective adaptive fixed-time control method is proposed by combining neural networks (NNs) technology and fixed-time theory. NNs are utilized to handle unknown nonlinearity and unmodeled parts in attitude systems. The hyperbolic tangent function is ushered to address the singularity problem that may occur in the derivative of the controller, thereby averting the phenomenon of chattering. For the sake of alleviating the correspondence burden of multiple UAVs attitude systems, a modified dynamic event-triggered mechanism (DETM) is ushered. The developed controller swears for that all signals of the six-rotor UAV attitude systems are bounded and the tracking errors converge to a small neighborhood of the origin within a fixed-time interval. Eventually, with the help of simulation results, the effectiveness of the proposed control scheme was verified.