Command filtered adaptive NN trajectory tracking control of underactuated autonomous underwater vehicles

In this paper, an adaptive neural network (NN) trajectory tracking control scheme is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and unknown disturbances. A novel additional control based on Nussbaum function is proposed to handle the underactuation problem of AUVs. The radial basis function NNs with minimal learning parameter (MLP) are employed to online approximate the compounded uncertain item due to unknown dynamic parameters and unknown disturbances. On the basics of the above, an adaptive NN trajectory tracking control law is proposed using command filtered vector-backstepping design tool. As a result, the computational burden of the developed trajectory tracking control scheme is significantly reduced. Theoretical analysis indicates that the proposed control law can force the AUV track the desired trajectory and guarantee that all signals in the trajectory tracking closed-loop control system are bounded. Simulation results on an AUV verify the effectiveness of our developed control scheme.