Adaptive Super-Twisting Distributed Formation Control of Multi-Quadrotor Under External Disturbance

Abstract

In this paper, the adaptive super-twisting distributed formation control of multi-quadrotor in the presence of external disturbances and uncertainties is studied. First, the quadrotor formation system is separated into a position subsystem and an attitude subsystem which are represented by unit-quaternions. And then a composite adaptive super-twisting control method is proposed for the position subsystem and attitude subsystem respectively. For the position subsystem, an adaptive multivariable super-twisting controller is designed such that the positions of formation converge to the desired formation configuration and generate the desired attitude. And the adaptive fast super-twisting controller is designed for the attitude subsystem to track the desired attitude in finite time. Based on Lyapunov-based stability analysis, finite time convergence stability of the whole closed-loop system is proved. Finally, a numerical simulation result is provided to illustrate the effectiveness of the proposed formation control scheme.

Exponential super-twisting control for nonlinear systems with unknown polynomial perturbations

“…Although the STA algorithm appeared in the 90s of the last century, it is still widely used today, such as the literatures 13,14,19,25,[29][30][31][32] of the last three years. The core structure of the STA algorithms used in these literatures is consistent.…”

“…For the systems subject to T-periodic perturbations, Papageorgiou and Edwards 19 investigate the stability properties and performance of super-twisting sliding-mode control loops. STA techniques were applied to different areas such as Mars entry trajectory tracking with nonsingular terminal sliding mode surface 20 , control of robot manipulators 21 , 22 and mobile robots 23 using robust high-order form, altitude control of a quadrotor unmanned aerial vehicle 24 , adaptive STA control of multi-quadrotor under external disturbance 25 , aircraft at high angle of attack 26 , STA control combined with radial basis function neural network for micro gyroscope 27 , energy management control for integrated DC micro-grid 28 , STA control of passive gait training exoskeleton driven by pneumatic muscles 29 , STA non-sigular fast terminal sliding motor control of interior permanent magnet synchronous motor 30 , and STA state observer based controllers for induction motor drive 14 and for permanent magnet synchronous motor drive 31 , 32 systems. Among these STA techniques, it is assumed that the system uncertainties or external perturbations are bounded within some constants and/or some Lipschitz functions with the boundaries known a priori.…”