摘要
This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data,a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm.To demonstrate the validity of the algorithm,an identification test is carried out on the ship‘SWAN’with only 26 sets of test data.Furthermore,the identification effects of the least squares algorithm,original stochastic gradient algorithm and the improved stochastic gradient algorithm based on nonlinear innovation are compared.Generally,the stochastic gradient algorithm is not suitable for the condition of small test data.The simulation results indicate that the improved stochastic gradient algorithm with sigmoid function greatly increases its accuracy of parameter identification and has 14.2%up compared with the least squares algorithm.Then the effectiveness of the algorithm is verified by another identification test on the ship‘Galaxy’,the accuracy of parameter identification can reach more than 95%which can be used in ship motion simulation and controller design.The proposed algorithm has advantages of the small test data,fast speed and high accuracy of identification,which can be extended to other parameter identification systems with less sample data.
基金
funded by the National Natural Science Foundation of China,grant number 51679024,51909018
the Science and Technology Innovation Fundation of Dalian City,grant number 2019J12GX026
the Fundamental Research Funds for the Central University,grant number 3132019343,3132021132
the University 111 Project of China,grant number B08046.
