Position-velocity constrained trajectory tracking control for unmanned underwater vehicle with model uncertainties

Faced with underwater missions within fixed area, unmanned underwater vehicles (UUVs) are often subject to environmental position constraints and their internal velocity constraints. This paper focuses on the trajectory tracking control problem of the UUVs with position-velocity constraints (PVCs) and model uncertainties. Different from the existing works, a novel adaptive position-velocity constrained tracking controller is designed for the UUVs. First, a nonlinear transformation function is employed on UUVs to facilitate directly constraining the position and velocity. Second, adaptive radial basis function neural networks (RBFNNs) are utilized to approximate the model uncertainties of the UUVs. By means of the Lyapunov stability theory, it is proved that the proposed control scheme can ensure all signals of the UUVs are semi-globally bounded and the PVCs are strictly maintained. Finally, both simulation and experiment results are given to validate the effectiveness and practicability of the proposed control scheme.