A process neural control algorithm for autonomous underwater vehicle
一种水下机器人运动的过程神经元控制

Process neural network is extended from a traditional neural network by adding an integration operator of time to better imitate the information processing system of a biological neuron. This is because both the input and output of motion control system for automatic underwater vehicles (AUVs) is processing vectors which is related with time. Combining the S function and the pre-planning idea, we develop a motion-control model for the process neuron of an AUV. Simulation results show that the new control is useful for underwater vehicles, featuring with higher precision, simpler design, faster response, and better robustness.