Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics

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With the trends towards autonomous shipping, advanced ship motion control methods have received increased attention in recent years. The validity of ship models is crucial in designing motion controllers and directly affects their performances. However, accurate models that could reflect true ship dynamics are highly nonlinear, complex and complicated to identify, especially in situations when the experimental conditions are limited. This paper proposes a data-driven predictive control method for path-following of under-actuated cargo ships with unknown dynamics, which makes use of data gathered during operation to improve the model and the path-following performance. Based on the ship navigation data set, the relations between the heading angle and the rudder angle of the ship are fitted with seven typical regression algorithms, which acts as the prediction model in the path-following controller. Simulation study is carried out to choose the most suitable regression algorithm, among which elastic net regression is selected. The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is introduced to find the optimal weights in the model identification process. A Line-of-Sight (LOS) algorithm is used as the guidance law to transform reference way-points into reference heading angles, and the path-following controller is designed also based on and the AMBS-P algorithm. Simulation results show that the proposed data-driven control method performs well in the path-following task without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. • The proposed data-driven control strategy allows the users to exploit the advantage of the predictive control scheme, as well as exploiting the approximate model knowledge captured from data without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. • A quantitative analysis of the model identification performances of seven typical regression algorithms is carried out. • The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is proposed and applied in finding the optimal weights in the model identification process and in designing the path-following controller with a LOS guidance principle. This could save the efforts spend in weight selection and controller parameter adjustments. It is also fast and easy to implement, which could facilitate its use in practice.