Numerical Simulation of Neural Network-Controlled Unmanned Undersea Vehicle

In this paper, the locomotion of an autonomously navigated undersea vehicle that uses vorticity control propulsion is computationally simulated. The navigation procedure employs a set of vehicle geometric and state variables to predict the needed vehicle body deformations in order to pass through a set of predefined path-points. To simulate the movement of the vehicle, a two-dimensional unsteady potential flow solver was developed based on the unsteady panel method coupled with the vehicle dynamics. The developed flow solver was validated against published computational results of unsteady flow standard test cases. Then, another set of properly planned test cases to cover the range of possible conditions were processed with the simulation and the output data was subsequently used to train a Multi-Layer Perceptron (MLP) neural network. The trained network can predict what body deflection time history is necessary for the vehicle to pass through the given path-points. Several autonomous navigation test cases are presented to show the capabilities of the present method. Key-Words:-Numerical Simulation, Unsteady Flow, Unsteady Panel Method, Unmanned Undersea Vehicle, Neural Network Control