An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control

Abstract

The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions was established, and the parameters of the model were initialized and updated by offline and online learning historical data. Using the autoregressive model with the acquired parameters, the prediction of the ship’s motion was achieved. Then, a Self-Stabilized system platform composed of six electric cylinders in parallel was designed, and the corresponding inverse kinematics were established. The corresponding controller using the result of motion prediction as the input was also proposed to counteract the extra motion variables of the ship. Various experiments, by simulating different sea conditions, can be carried out. The results show that the average error of the motion prediction was less than 1%. The maximum error of the self-stabilizing control was 1.6°, and the average error was stable within 0.7°. The Self-Stabilized system was able to effectively compensate for the rocking motion of ships affected by waves, which was of great significance for improving the maritime safety guarantee and the intelligent level of shipborne equipment.

Kinematic Calibration for the 3-UPS/S Shipborne Stabilized Platform Based on Transfer Learning

“…For oceanographic research vessels engaged in marine observation missions with scientific instruments, the 3-DOF motion-roll, pitch, and yaw-leads to attitude variations that significantly impact the measurement accuracy of the scientific instruments. Utilizing a 3-DOF robot as a shipborne attitude-stabilized platform to compensate for the vessel's attitude changes in real-time is considered an effective engineering solution [1].…”