Trajectory tracking of underwater vehicle based on Back-Stepping neural network adaptive robust sliding mode control

Complex marine environment and modeling uncertainties have a great impact on the precise control of the marine vehicles. A novel adaptive sliding mode controller, namely BRS-controller, is proposed based on back-stepping and RBF neural network. The back-stepping technique is employed to reduce the order of the underwater vehicle system by introducing the virtual speed to the system kinematic model. An adaptive robust sliding mode rule is presented based on the system dynamic model to improve the system’s robust performance. In addition, the RBF neural network theory is used to approximate the modeling uncertainties of the underwater vehicle system. The stability of the system is analyzed based on Lyapunov theory. The numerical simulation results demonstrate the effectiveness and robustness of the proposed BRS-Controller.