Neural networks based parking control of deep-sea HydroThermal Plume Explorer

Depth parking control is important for the HTPE (HydroThermal Plume Explorer). It aims to have a more accurate parking depth and a less oscillation at the target depth. The plant approaching and motion forecasting model based on RBF neural network is built to self-adjust and approach online motion law of HTPE in ideal circumstance of the sea, and simulate output of given control quantity after an PID control period. Together with RBF neural network model, BP neural network based self-adaptive PID control model is used for rectification of PID parameters and for holding the parking depth. Finally, simulations testify that neural networks based control method is feasible.