Neural prescribed-time dynamic positioning control of semi-submersible platforms with asymmetric input saturation

Abstract

This paper presents an adaptive prescribed-time control scheme to the dynamic positioning (DP) system of semi-submersible platforms (SSPs) in the presence of asymmetric input saturation. To eliminate the adverse effect induced by the input saturation, a saturated compensating auxiliary system is introduced. The system singularity problem is removed by automatically enlarging and recovering the velocity error by implanting a modification saturated signals into the velocity error. The dynamic errors are transformed into a new error variable by using a fixed-time tracking performance function (FTTPF). After that, the fixed-time funnel boundaries (FTFBs) will no longer need to be redesigned according to various initial attitude errors. Meanwhile, the trajectories of the attitude errors are limited to the designed boundaries over a finite time interval. In addition, the new errors in the closed-loop system are guaranteed to be semi-global uniformly ultimately bounded (SGUUB). Finally, two simulations are performed to illustrate the effectiveness and superiority of the proposed scheme.