Neural networks modeling of autonomous underwater vehicle

This paper describes two different neural networks models for autonomous underwater vehicles (AUVs). The online multilayer perceptron neural networks (OMLPNN) have been designed to perform modeling of AUVs of which the dynamics are highly nonlinear and time varying. The online recurrent multilayer perceptron neural networks (ORMLPNN) have been additionally designed to generate a memory to pervious states and increase the performance of the modeling. The designed OMLPNN and ORMLPNN with the use of backpropagation learning algorithm has advantages and robustness to model the highly nonlinear functions. The proposed neural networks architectures have been designed to model the test bed for AUV named NPS AUV. Simulation results show effectiveness of the OMLPNN and ORMLPNN to deal with modeling of AUVs as it has good capability to incorporate the dynamics of the system.