Analysis and Identification of Automatic Intelligent Submarine Parameters with AUV System using Particle Cumulative Intelligence Training Algorithm

Searching in the depths of the waters is one of the most important human needs to discover the underwater world and creatures and resources. For this action, the need for submarines is felt very much, especially the automatic control submarine that protects human life against unpredictable phenomena. Do not risk its guidance. In order to control automatic subsurface vehicles (AUV), it is necessary to identify its precise dynamic model. Obtaining the model is the first step in this field and identifying the parameters of the model is the next step. This is very complicated and time-consuming due to the nonlinear structure and coupling of model parameters. One of the biggest challenges in this field is the preparation of basic information about the movement of the detected subsurface float. In this research, we introduced a new method of identifying the model of subsurface vessels, which has always been involved with a lot of parameter uncertainty. The above algorithm works in such a way that first, with the help of adaptive fuzzy methods, it creates the structure of the fuzzy model of the relevant system, and by using the training algorithm of cumulative intelligence of particles and improving the parameters of the membership functions of the fuzzy model, the optimal model is obtained. The subsurface sample float investigated in this research is of NPS type and has six degrees of freedom of movement in space. Also, the results of simulations performed on real data from an available AUV sample have been presented.

کلیدواژه ها:

Automatic Subsurface Vehicles ، AUV System ، Particle Cumulative Intelligence System (PSO) ، NPS AUV ، Cumulative Intelligence Algorithm