Adaptive Neural Network Dynamic Surface Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles

Aiming at the trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) with unknown dynamic model parameters and time-varying ocean disturbances, a dynamic surface adaptive control strategy based on radial basis function neural network is proposed to complete the desired three-dimensional trajectory tracking. Firstly, an output redefinition method is used to solve the underactuated problem. Secondly, a dynamic surface control method is used to overcome the “explosion of complexity” problem in traditional backstepping control, and complexity of the control law design is simplified. At the same time, a nonlinear damping term is added in control law to overcome unknown external disturbances. Finally, unknown dynamic model parameters are approximated by a neural network, and the neural network weights are estimated online by an adaptive law. The Lyapunov theory is used to prove that all signals of closed-loop system are semi-globally uniformly bounded, and effectiveness of the control law is verified by comparative simulations.