Abstract
This paper focuses on the ship adaptive dynamic positioning output feedback control considering the thruster system dynamics, solving the problems of the ship output end affected by measurement noise, unmeasurable ship speed, unknown time-varying disturbance, model parameter ingestion, and input saturation simultaneously. First, a high-gain state observer is used for online estimation of the unknown ship speed, and an update gain technique is introduced to ensure the speed of state construction and reduce the effect of measurement noise. In addition, a single-parameter learning neural network is used to solve the unknown time-varying disturbance and ship model parameter ingestion problem. Furthermore, the thruster system dynamic equation is considered to solve the thruster system dynamic characteristics. Finally, the finite-time auxiliary dynamic system is used to deal with the input saturation problem. The analysis process shows that the control method can make the ship finally reach and stay in the desired position and desired heading, and all control variables in the designed dynamic positioning system are consistent and eventually bounded. The simulation results show that the proposed dynamic positioning control method is effective.
Published Version
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