Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation

In this paper, the trajectory tracking control of the autonomous underwater vehicle (AUV) has been investigated in discrete time, for ease of digital computer calculation. A reinforcement learning scheme is employed using two neural networks, whereas the first one is to compensate for uncertainties for the controller, and the second one is to estimate the evaluation function, such that optimal tracking performance could be achieve for the AUV. Simulation results show that the errors convergence to a adjustable neighborhood around zero, and optimization has been achieved in the sense of reinforcement learning.