Parameter identification of smart float diving model based on ASNLS algorithm
[Objectives]Aiming at the challenge of accurate diving modeling of a smart float,an anti-saturation and noise least squares(ASNLS)algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction.[Methods]Firstly,the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model,and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling.Subsequently,the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise.Finally,by adjusting the values of the covariance matrix,the designed diving parameter identification algorithm achieved resistance to data saturation.[Results]Based on the data of the South China Sea deep diving experiment of the smart float in 2021,diving model parameter identifica-tion and depth prediction are carried out.The results demonstrate that the ASNLS algorithm has faster conver-gence speed(31.8%higher than the least squares algorithm)and smaller depth prediction error(average abso-lute percentage errors less than 9%at different depths)than both the traditional least squares algorithm and supports the vector machine algorithm.[Conclusions]Consequently,the ASNLS algorithm can provide ef-fective support for the depth control and prediction of the smart float.
smart floatparameter identificationantisaturation and noise least squares(ASNLS)al-gorithmmotion predictiondata saturation
万方数据
维普
