Multi-objective optimal input design for grey-box identification modelling of ship manoeuvring motion

Grey-box modelling has been widely used to predict the ship manoeuvring motion. In order to design input signals for the grey-box model of ship manoeuvring motion, a multi-objective optimal input design (MOID) method is proposed in this paper. In the proposed method, the MOID is regarded as a multi-objective optimization problem (MOP), where both of D-optimality criterion and condition number are taken as two-objective functions to improve the accuracy and robustness of identifying the grey-box model. The correlation factor is also presented as a constraint for reducing the parameter drift. The MOP is solved by the non-dominated sorting genetic algorithm-II (NSGA-II) with a constraint handling technique. The technique for order preference by similarity to an ideal solution (TOPSIS) is also adopted to screen out an optimal solution from the Pareto solutions. The effectiveness of MOID signals is validated by Monte Carlo analysis and grey-box identification of the MMG model.