PIANC Smart Rivers 2022

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In order to realize the parameter identification of the ship maneuvering motion model and the adaptive track tracking control, the second-order nonlinear response model of the ship is used as the research object, and the training is constructed from the "Z"-shaped test data of the 10°/20°/30° self-propelled ship model For the sample pair, the least squares support vector machine method was used to identify the ship maneuvering motion model parameters. At the same time, the maneuverability prediction simulation was carried out, and compared with the actual test data, the simulation results verified that the least square support vector machine method is in the parameter identification On the effectiveness. Furthermore, on the basis of parameter identification, this paper takes into account the maneuverability of the ship to carry out the trajectory tracking control of the self-propelled ship model, optimizes the LOS navigation algorithm, and determines the switch to the next waypoint based on the ship rudder angle, speed, waypoint and other information Time, so as to realize the ship's adaptive track tracking control. In this paper, the course keeping test of the self-propelled ship model at different speeds is carried out. Through the experiment, the relationship between the PID control parameters and the ship maneuvering motion model parameters is summarized, and the corresponding PID control parameters are applied to the self-propelled ship model’s track tracking test for heading control. The optimized LOS navigation algorithm is verified by the self-propelled ship model track tracking test. The test results show that the optimized LOS navigation algorithm has high accuracy in track tracking control.

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