Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV’s on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1–97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.

This paper deals with the real-time tracking control problem for an autonomous underwater vehicle based on an acoustic-based positioning method, i.e., the so-called GPS intelligent buoy system, which causes inevitable measurement delay. The measurement delay increases the control difficulty and degrades the tracking accuracy. Additionally, the exact modeling for an autonomous underwater vehicle is difficult due to uncertain hydrodynamic parameters. Based on these findings, a model-free control scheme is proposed. In the proposed scheme, the GPS intelligent buoy system provides the position signals without velocity measurements. Considering the measurement noise, a robust exact differentiator is used instead of the traditional numerical differentiation method to obtain the derivatives of position signals, which saves the limited actuator energy of autonomous underwater vehicles. Simulations are performed to verify the validity of the proposed control scheme. The results demonstrate that the proposed control scheme can achieve high timeliness and high tracking accuracy for autonomous underwater vehicles. Compared to the conventional model predictive control, the proposed controller requires 89.7% less average calculation time. In addition, the proposed controller outperforms the conventional proportion-differentiation controller in root-mean-square error by approximately 62.3−80.7%.