Adaptive neural network visual servo control for dynamic positioning of underwater vehicles

Dynamic positioning is an important technique to ensure the capability of a next generation underwater vehicle to fulfill complex intervention and observation tasks. This work proposes an adaptive neural network image-based visual servo (IBVS) controller for positioning an underwater vehicle with respect to a fixed target in camera-in-hand configuration. The visual servoing error is defined by projecting the image error to the vehicle space using a pseudo inverse of the estimated Jacobian matrix. In the proposed dual-loop system architecture, the velocity command is designed to stabilize the visual servoing error in the kinematic loop, and the desired acceleration is derived to track the velocity command in the dynamic loop. A single hidden layer (SHL) feedforward neural network, in conjunction with a conventional proportional-integral (PI) controller, is applied to the cascaded systems to compensate for the uncertainties in both the Jacobian matrix and the vehicle dynamics. The thruster control signals are obtained by using the approximate dynamic inversion (ADI) of the vehicle. The uniform ultimate boundedness of the visual servoing error and the neural network weight matrices are proved by Lyapunov theory. Simulation results are provided to verify the effectiveness of the controller with two candidate methods of estimating the image Jacobian matrix.