Diving control of autonomous underwater vehicle based on adaptive backstepping method

In order to implement precise diving control of the autonomous underwater vehicle (AUV), according to the kinematic and nonlinear dynamic model of AUV, an adaptive iterative backstepping method based on neural network is proposed, and a kinematic and dynamic controller is designed. In the investigation, considering the existence of attack angle and the uncertainties of hydrodynamic damping parameters of the nonlinear model of AUV, a neural network-based controller is designed to on-line estimate the nonlinear hydrodynamic damping terms existing in the pitch motion together with external ocean current disturbances. Then, the adaptive law of the network weights is presented based on the Lyapunov stability theory to guarantee the uniform ultimate bounding of all signals in the closed-loop system. Finally, two groups of simulation experiments are carried out to compare the system response of the designed controller at a certain control gain and the diving control performance in the presence of disturbances. The results show that the designed controller is of smaller static error and higher tracking precision.