Biomimetic Underwater Vehicle Modeling Based on Neural Network

This paper introduces the basic control methods of a Biomimetic Underwater Vehicle, and a neural network model is designed for the vehicle. The concerned vehicle is propelled by two undulating long-fins installed on both sides. Ten motors are employed to drive the long-fin and sine wave function is employed for motor control. The real-time control system is designed for controlling the long-fins by adjusting its oscillating frequency and oscillating amplitude to control the yaw angle of the vehicle. A set of inertial sensors is employed for collecting pose information including yaw angle. Analyses show that the yaw angle increment is closely related to the long-fin oscillating frequency, the long-fin oscillating amplitude and the vehicle yaw angle velocity. Hence a neural network with one hidden layer is designed for building a nonlinear MISO model. Based on qualitative hydrodynamic analysis, oscillating frequency of the two long-fins and angular velocity are chosen as the model input. And the yaw angle increment is chosen as the model output. Based on analysis of the experimental thrust data, linear function is used as activation function of the neurons. Finally, experimental data are used for neural network training and validation. The results show that the designed neural network model is valid.