Trajectory tracking of autonomous underwater vehicle under disturbance based on time-delay adaptive high-order sliding mode control

This paper introduces an innovative adaptive generalized super twisting algorithm (AGSTA) controller combined with a time-delay estimator (TDE) to address the trajectory-tracking issue in autonomous underwater vehicles (AUVs) impacted by disturbances. AUVs utilized in underwater exploration often carry sensors, such as Doppler velocity logs, for position and speed determination. However, owing to instrument precision limitations and other factors, these sensors suffer from a slow sampling speed. Furthermore, manual tuning requirement, conventional GSTAs do not provide substantial benefits in real-world applications, adversely affecting AUV position control. To rectify this issue, we proposed a TDE founded on delayed sensor data to estimate the vehicle fluid dynamics, integrated with an AGSTA, to enhance the controller performance. A comprehensive stability analysis of the control system was carried out under these parameters. The effectiveness of the proposed method was demonstrated through simulation results. •An adaptive generalized super twisting algorithm (AGSTA) is proposed.•The AGSTA+TDE controller ensures robust tracking without manual adjustment.•The stability of AGSTA+TDE controller is verified.•The simulation results show the superiority of the scheme.