Nonlinear Identification for 4-DOF Ship Maneuvering Modeling via Full-Scale Trial Data
Chunyu Song, Xianku Zhang, Guoqing Zhang
Abstract
This research involves a 4-DOF ship maneuvering modeling with full-scale trial data. In order to avert the inversion of the multi-innovation matrix in the traditional multi-innovation least squares algorithm, a new novel is proposed based on the recognition concept new multi-innovation least squares algorithm to identify the innovation of the stochastic gradient hyperbolic tangent nonlinearity. A lot of work and efforts have been made to ensure the consistency and final convergence. Combined with relevant data and statistical indicators, the author derives a more effective hyperbolic tangent nonlinear innovation identification scheme to identify ship maneuvering motion. Compared with the previous results, this design scheme has significant computational advantages, with higher accuracy, faster identification speed, higher computational efficiency, and requiring fewer parameters. At the same time, the example is given to illustrate the effectiveness of the algorithm, especially for identification applications with full-scale trial data.