Abstract
A stable adaptive control scheme for multi-point mooring system (MPMS) with uncertain dynamics is proposed in this paper. The control scheme is designed by a hybrid controller based on RBF (Radial Basis Function) NN (Neural Network) and SMC (Sliding Mode Control), which learns the MPMS dynamic changes, and the compensation of external disturbances is realized through adaptive RBFNN control. Meanwhile the RBF-SMC control parameters are adapted by the Lyapunov method to minimize squares dynamic positioning (DP) error. The convergence of the hybrid controller is proved theoretically, and the proposed mooring control scheme is applied to the “Kantan3” mooring simulation system. Finally, the simulation results are compared with the traditional PID controller and standard RBF controller to demonstrate the effective mooring positioning performance of the control scheme for the MPMS.
Highlights
Many kinds of offshore structures, such as mooring positioning system (MPMS), dynamic positioning (DP) system, and anchor auxiliary dynamic positioning system are being widely used in semi-submersible production platform
The research of deepwater mooring systems is mainly reflected in the dynamic analysis and the study of mooring damping. [2] considered the influence of nonlinear factors such as inertia, damping, and elastic deformation of anchor cable, the concentrated mass method is extended from the frequency domain to the time domain. [3] employed the time domain method to numerically analyze the dynamic effects of nonlinear coupling and uncoupling between the Spar platform’s main body and the mooring system
In order to verify the effects of the RBF-SMC controller, the semi-submersible offshore platform “Kantan3” is the research object and simulated
Summary
Many kinds of offshore structures, such as mooring positioning system (MPMS), dynamic positioning (DP) system, and anchor auxiliary dynamic positioning system are being widely used in semi-submersible production platform. Among these platforms, the mooring system is characterized by less investment and convenience with maintenance; it is the main positioning system [1]. The initial DP control system added a low-pass filter to the traditional PID control model; the positioning accuracy of the controller is affected To solve this problem, [6] proposed an approach that integrates optimal control with the principle of Kalman filters, and the actual scale was verified later by [7]. To solve its nonlinear problems, some methods have been applied to the DP
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