Neural networks control of autonomous underwater vehicle

This paper describes a neural network controller for autonomous underwater vehicles (AUVs). The designed online multilayer perceptron neural network (OMLPNN) calculates forces and moments in earth fixed frame to eliminate the tracking errors of AUVs whose dynamics are highly nonlinear and time varying. Another OMLPNN has been designed to generate an inverse model of AUV, which determine the appropriate propeller´s speed and control surfaces´ angles receiving the forces and moments in the body fixed frame. The designed approximation based neural network controller with the use of the backpropagation learning algorithm has advantages and robustness to control the highly nonlinear dynamics of AUV. The proposed neural networks architectures have been designed to control the test bed for AUV named NPS AUV. The Simulation results showed effectiveness of the OMLPNN to deal with elimination of AUVs´ tracking errors as it has good capability to incorporate the dynamics of the system.