Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles

In this paper, a hybrid visual servo (HVS) controller is proposed for underwater vehicles, in which a combination of the vehicle's 3-D Cartesian pose and the 2-D image coordinates of a single feature is exploited. A dynamic inversion-based sliding mode adaptive neural network control (DI-SMANNC) method is developed for tracking the HVS reference trajectory generated from a constant target pose. A single hidden-layer (SHL) feedforward neural network, in conjunction with an adaptive sliding mode controller, is utilized to compensate for dynamic uncertainties. The adaptation laws of neural network weight matrices and control gains are designed to ensure the asymptotical stability of tracking errors and the ultimate uniform boundedness (UUB) of neural network weight matrices. The main advantage of the proposed DI-SMANNC over conventional sliding model neural network controllers lies in the fact that the knowledge of the bounds on system uncertainties and neural approximation errors is not required to be previously known. Simulation results are presented to validate the effectiveness of the developed controller, especially the robustness with respect to dynamic modeling uncertainties and camera calibration errors.