Hongrui Zhang

A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral “saturation” problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.

In view of the requirements on control precision of autonomous underwater vehicles (AUVs) in different operations, the improvement of AUV motion control accuracy is the focus of this paper. In regard to the unsatisfying robustness of traditional control methods, an interactive network based on Least Square Support Vector Regression (LSSVR) is therefore put forward. The network completed the identification of the strong nonlinear AUV dynamic characteristics based on the LSSVR theory and by virtue of the interactions between the offline and online modules, it achieved offline design and online optimization of the AUV control law. In addition to contrastive numerical simulations and sea trials with the classic S-plane method in AUV velocity and heading control, the LSSVR network was also tested in path following and long-range cruise. The precision and robustness and of the proposed network were verified by the high-accuracy control results of the aforesaid simulations and trials. The network can be of practical use in AUV control especially under unfamiliar water conditions with access to a limited number of control samples or little information of the operation site.